jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weixin Chen | Dawn Song | Bo Li
Source: arXiv (Cornell University) (preprint, 2023-03-10)
Field: Materials Science — Materials Chemistry

TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets Diffusion models have achieved great success in a range of tasks, such as image synthesis and molecule design. As such successes hinge on large-scale training data collected from diverse sources, the trustworthiness of these collected data is hard to control or audit. In this work, we aim to explore the vulnerabilities of diffusion models under potential training data manipulations and try to answer: How hard is it to perform Trojan attacks on well-trained diffusion models? What are the adversarial targets that such Trojan attacks can achieve? To answer these questions, we propose an effective Trojan attack against diffusion models, TrojDiff, which optimizes the Trojan diffusion and generative processes during training. In particular, we design novel transitions during the Trojan diffusion process to diffuse adversarial targets into a biased Gaussian distribution and propose a new parameterization of the Trojan generative process that leads to an effective training objective for the attack. In addition, we consider three types of adversarial targets: the Trojaned diffusion models will always output instances belonging to a certain class from the in-domain distribution (In-D2D attack), out-of-domain distribution (Out-D2D-attack), and one specific instance (D2I attack). We evaluate TrojDiff on CIFAR-10 and CelebA datasets against both DDPM and DDIM diffusion models. We show that TrojDiff always achieves high attack performance under different adversarial targets using different types of triggers, while the performance in benign environments is preserved. The code is available at https://github.com/chenweixin107/TrojDiff. Trojan ; Computer science ; Domain (mathematical analysis) ; Diffusion ; Class (philosophy) ; Process (computing) ; Adversarial system ; Theoretical computer science ; Code (set theory) ; Artificial intelligence ; Computer security ; Programming language ; Mathematics ; Physics ; Set (abstract data type) Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning
Authors: Maxim Lysov | Konstantin Pukhkiy | Evgeny Vasiliev | Alexandra Getmanskaya | Вадим Турлапов
Source: Preprints.org (preprint, 2023-01-06)
Field: Chemistry — Analytical Chemistry

Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-diagnostics of Plant Stress The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions. Curse of dimensionality ; Dimensionality reduction ; Artificial neural network ; Computer science ; Artificial intelligence ; Hyperspectral imaging ; Reduction (mathematics) ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Sergey Kucheryavskiy | Sergei Zhilin
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Chemistry — Analytical Chemistry

Collinear datasets augmentation using Procrustes validation sets In this paper, we propose a new method for the augmentation of numeric and mixed datasets. The method generates additional data points by utilizing cross-validation resampling and latent variable modeling. It is particularly efficient for datasets with moderate to high degrees of collinearity, as it directly utilizes this property for generation. The method is simple, fast, and has very few parameters, which, as shown in the paper, do not require specific tuning. It has been tested on several real datasets; here, we report detailed results for two cases, prediction of protein in minced meat based on near infrared spectra (fully numeric data with high degree of collinearity) and discrimination of patients referred for coronary angiography (mixed data, with both numeric and categorical variables, and moderate collinearity). In both cases, artificial neural networks were employed for developing the regression and the discrimination models. The results show a clear improvement in the performance of the models; thus for the prediction of meat protein, fitting the model to the augmented data resulted in a reduction in the root mean squared error computed for the independent test set by 1.5 to 3 times. Collinearity ; Categorical variable ; Resampling ; Computer science ; Latent variable ; Pattern recognition (psychology) ; Data set ; Mean squared error ; Artificial intelligence ; Data mining ; Statistics ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Omar M El‐Abassy | Israa M. Nour | Mohamed Badrawy
Source: ERU Research Journal (article, 2023-08-23)
Field: Chemistry — Analytical Chemistry

Review on different analytical techniques for analysis of atenolol alone or in combination with other antihypertensive drugs in different matrices Several analytical techniques were reported for the analysis of atenolol (antihypertensive) either in pharmaceutical dosage forms or in biological samples by various analytical techniques including HPLC, spectrophotometry, fluorimetry, and electrochemical methods. This article includes important information about the cited drug involving physical and chemical characteristics in addition to its pharmacological actions. Atenolol is a β-adrenoreceptor blocker that is used for hypertension, myocardial infarction, and angina. It works via inhibition of renin release, angiotensin -II, and aldosterone production. Atenolol is combined with several antihypertensive agents like losartan, amlodipine, and nifedipine. This review is concerned with different analytical methods of atenolol either alone or with others. The most common method that is used for the analysis of the studied drug is the chromatographic technique then spectroscopy. Fluorimetric and electrochemical methods were used mostly for the determination of atenolol in biological fluids. All methods were validated according to ICH. Atenolol ; Pharmacology ; Medicine ; Internal medicine ; Blood pressure Analytical Methods in Pharmaceuticals
Authors: Gábor Kónya | Zsolt Ferenc Kovács | J. Takács
Source: Gradus (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A szerszáméltartamot befolyásoló tényezők nikkel-bázisú szuperötvözetek horonymarásakor Jelen publikációban a szerszáméltartamot befolyásoló tényezőket mutatjuk be, rendszerezzük és illesztjük Nikkelbázisú szuperötvözetekhez annak érdekében, hogy a kutatási célok egyértelműen meghatározhatók legyenek. Ehhez először feltárjuk a megmunkálandó alapanyag tulajdonságait, melyhez a többi tényezőt illeszteni tudjuk. Továbbiakban feltárjuk, hogy az adott területeken mások mit értek el, mik az előnyeik és meghatározásra kerülnek azon területek, melyeket vizsgálni kell. Mathematics Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Polymer Science and Applications
Authors: Dylan Xavier | Natasha Lucas | Steven G. Williams | Jennifer M.S. Koh | Keith Ashman | Clare Loudon | Roger R. Reddel | Peter G. Hains | Phillip J. Robinson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-28)
Field: Chemistry — Spectroscopy

Heat ‘n Beat: A universal high-throughput end-to-end proteomics sample processing platform in under an hour Abstract Proteomic analysis by mass spectrometry (MS) of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a near universal, rapid and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centred on a one tube homogenisation and digestion method called Heat ‘n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided to separate challenges. The initial sample washing, and final peptide clean-up steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin-embedded (FFPE). Thirdly, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenisation treatment, and reduction, alkylation and proteolysis were optimised into a single step. The refinements produced near doubled peptide yield, delivered consistently high digestion efficiency of 85-90%, and required only 38 minutes for core processing in a single tube, with total processing time being 53-63 minutes. The robustness of HnB was demonstrated on six organ types, a cell line and a cancer biopsy. Its suitability for high throughput applications was demonstrated on a set of 1,171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 hours, producing highly consistent peptide yield and quality for over 3,513 MS runs. Graphical Abstract Sample preparation ; Lysis ; Chromatography ; Mass spectrometry ; Chemistry ; Computer science ; Biomedical engineering ; Materials science ; Biochemistry ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Richa Dubey | Velmathi Guruviah | Ravi Prakash Dwivedi
Source: Journal of Nano- and Electronic Physics (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Ensemble Approach for Capacitance Prediction of Heteroatom Doped Carbon Based Electrode Materials Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine) Random forest ; Mean squared error ; Artificial intelligence ; Perceptron ; Computer science ; Regression ; Root mean square ; Statistics ; Mean squared prediction error ; Classifier (UML) ; Pattern recognition (psychology) ; Mathematics ; Machine learning ; Artificial neural network ; Physics Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors
Authors: Dong Chen | Jian-ming Gao | Danting Zheng | Zhiheng Guo | Zuncheng Zhao
Source: Molecules (article, 2023-12-14)
Field: Chemistry — Organic Chemistry

Gas Phase Conformation of Trisaccharides and Core Pentasaccharide: A Three-Step Tree-Based Sampling and Quantum Mechanical Computational Approach As an important component of N-linked glycoproteins, the core pentasaccharide is highly crucial to the potential application prospect of glycoprotein. However, the gas phase conformation study is a challenging one due to the size and complexity of the molecule, together with the necessity to rely on quantum chemistry modeling for relevant energetics and structures. In this paper, the structures of the trisaccharides and core pentasaccharides in N-linked glycans in the gas phase were constructed by a three-step tree-based (TSTB) sampling. Since single point energies of all the conformers are calculated at the temperature of zero, it is necessary to evaluate the stability at a high temperature. We calculate the Gibbs free energies using the standard thermochemistry model (T = 298.15 K). For trimannose, the energetic ordering at 298.15 K can be strongly changed compared to 0 K. Moreover, two structures of trimannose with high energies at 0 K are considered to provide a much better match of IR vibration signatures with the low Gibbs free energies. On this basis, the core pentasaccharide was constructed in three ways. The building configurations of core pentasaccharide were optimized to obtain reasonable low-energy stable conformers. Fortunately, the lowest-energy structure of core pentasaccharide is eventually the minimum at 0 K and 298.15 K. Furthermore, spectrum analysis of core pentasaccharide was carried out. Although poorly resolved, its contour from the experiment was in qualitative correspondence with the computed IR spectrum associated with its minimum free energy structure. A large number of strongly and weakly hydrogen-bonded hydroxyl and acetylamino groups contribute to a highly congested set of overlapping bands. Compared with traditional conformation generators, the TSTB sampling is employed to efficiently and comprehensively obtain preferred conformers of larger saccharides with lower energy. Chemistry ; Conformational isomerism ; Gibbs free energy ; Core (optical fiber) ; Molecule ; Quantum ; Computational chemistry ; Thermodynamics ; Crystallography ; Stereochemistry ; Physics ; Organic chemistry ; Quantum mechanics Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Protein Structure and Dynamics
Authors: Daniel R. Mason | D. Nguyen-Manh | Victor Lindblad | Fredric Granberg | M. Yu. Lavrentiev
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

An empirical potential for simulating hydrogen isotope retention in highly irradiated tungsten We describe the parameterization of a tungsten-hydrogen empirical potential designed for use with large-scale molecular dynamics simulations of highly irradiated tungsten containing hydrogen isotope atoms, and report test results. Particular attention has been paid to getting good elastic properties, including the relaxation volumes of small defect clusters, and to the interaction energy between hydrogen isotopes and typical irradiation-induced defects in tungsten. We conclude that the energy ordering of defects changes with the ratio of H atoms to point defects, indicating that this potential is suitable for exploring mechanisms of trap mutation, including vacancy loop to plate-like void transformations. Tungsten ; Molecular dynamics ; Hydrogen ; Materials science ; Vacancy defect ; Crystallographic defect ; Irradiation ; Isotope ; Void (composites) ; Chemical physics ; Interatomic potential ; Relaxation (psychology) ; Atomic physics ; Radiochemistry ; Chemistry ; Crystallography ; Computational chemistry ; Nuclear physics ; Physics ; Metallurgy ; Composite material Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Wenqi Lou | Luiz F. Brito | Xiuxin Zhao | Jianbin Li | Yachun Wang
Source: Preprints.org (preprint, 2023-10-20)
Field: Chemistry — Analytical Chemistry

Selection of the Most Informative Wavenumbers to Improve Prediction Accuracy of Milk Fatty Acid Profile Based on Milk Mid-Infrared Spectra Data Milk MIR spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test-day records, and reference FA concentrations of 155 first-parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and five feature selection methods were evaluated. The results indicated that the Competitive Adaptive Reweighted Sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables [days in milk at the test day, age at the test day, pregnancy stage (in days), number of days open, number of inseminations, and somatic cell count] yielded the best FA profile predictions based on Partial Least Square regression. In particular, ten FAs (C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium-chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA) presented accuracies based on the determination coefficient (R2cv) ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. By running CARS 1,000 times in internal validations, we obtained the frequency of selected milk MIR wavenumber for 35 FAs. The most related wavenumbers to FAs were found within 1,003 to 1,145 cm-1, while other discrete areas were between 1,651 to 1,797 and 2,834 to 2,954 cm-1. These biomarkers may give insights into the relationship between MIR spectra and FA phenotypes. In conclusion, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available. Feature selection ; Interpretability ; Statistics ; Robustness (evolution) ; Mathematics ; Polyunsaturated fatty acid ; Fatty acid ; Chemistry ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Milk Quality and Mastitis in Dairy Cows
Authors: Nick Ruben Neuling | Robin D. Allert | Dominik B. Bucher
Source: ChemRxiv (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

Prospects of Single-Cell NMR Spectroscopy with Quantum Sensors Single-cell analysis can unravel functional heterogeneity within cell populations otherwise obscured by ensemble measurements. However, non-invasive techniques that probe chemical entities and their dynamics are still lacking. This challenge could be overcome by novel sensors based on nitrogen-vacancy (NV) centers in diamond, which enable nuclear magnetic resonance (NMR) spectroscopy on unprecedented sample volumes. In this perspective, we briefly introduce NV-based quantum sensing and review the progress made in microscale NV-NMR spectroscopy. Lastly, we discuss approaches to enhance the sensitivity of NV ensemble magnetometers to detect biologically relevant concentrations and provide a roadmap towards their application in single-cell analysis. Microscale chemistry ; Nuclear magnetic resonance spectroscopy ; Spectroscopy ; Quantum sensor ; Nuclear magnetic resonance ; Magnetometer ; Vacancy defect ; Nanotechnology ; Materials science ; Chemical physics ; Quantum ; Physics ; Magnetic field ; Quantum computer ; Quantum network ; Quantum mechanics Diamond and Carbon-based Materials Research ; Advanced Memory and Neural Computing ; Magnetic properties of thin films
Authors: Anne Cathrine Kufner | Sarina Zink | Kerstin Wohlgemuth
Source: Preprints.org (preprint, 2023-12-30)
Field: Materials Science — Materials Chemistry

Speeding-Up Process Development of Continuous Slug Flow Crystallization for Novel Substance Systems The pharmaceutical industry is undergoing a shift in processing methods for small-scale production (250-1000 kg/a), leading to an increased focus on continuous production research. Various concepts were developed to overcome challenges in small-scale crystallization for the production of high-quality products. However, further optimization is needed to enhance the industrial applicability of these concepts. This study aims to demonstrate the practical applicability of the Slug Flow Crystallizer (SFC) in the pharmaceutical industry by extending its use to a wider range of substance systems for small-scale API production. Employing an experimental strategy, the research seeks to rapidly assess the SFC&amp;#039;s potential for achieving uniform, high-purity product yields, emphasizing the importance of factors such as slug flow stability, residence time distribution, and substance-specific crystallization criteria. This, coupled with our previously published model-based strategy, forms a comprehensive methodology to explore crystallization behavior and modeling for a variety of substance systems within the SFC. The ultimate goal of this study is to showcase the practicality of the SFC in the pharmaceutical industry, enabling the way for its reliable use in long-term, small-scale continuous crystallization and the production of high-quality pharmaceutical products. Crystallization ; Scale (ratio) ; Quality (philosophy) ; Process engineering ; Continuous flow ; Process (computing) ; Residence time distribution ; Biochemical engineering ; Computer science ; SCALE-UP ; Production (economics) ; Flow (mathematics) ; Engineering ; Mathematics ; Chemical engineering Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Freezing and Crystallization Processes
Authors: Christina Vallentin Holler | Nina Møller Petersson | Malene Brohus | Miska Aleksanteri Niemelä | Emil Drivsholm Iversen | Michael T. Overgaard | Hideo Iwaï | Reinhard Wimmer
Source: Cell Calcium (article, 2023-11-14)
Field: Materials Science — Materials Chemistry

Allosteric changes in protein stability and dynamics as pathogenic mechanism for calmodulin variants not affecting Ca2+ coordinating residues Mutations in the small, calcium-sensing, protein calmodulin cause cardiac arrhythmia and can ultimately prove lethal. Here, we report the impact of the G113R variant on the structure and dynamics of the calmodulin molecule, both in the presence and in the absence of calcium. We show that the mutation introduces minor changes into the structure of calmodulin and that it changes the thermostability and thus the degree of foldedness at human body temperature. The mutation also severely impacts the intramolecular mobility of calmodulin, especially in the apo form. Glycine 113 acts as an alpha-helical C-capping residue in both apo/ - and Ca<sup>2+</sup>/calmodulin, but its exchange to arginine has very different effects on the apo and Ca<sup>2+</sup> forms. The majority of arrhythmogenic calmodulin variants identified affects residues in the Ca<sup>2+</sup> coordinating loops of the two C-domain EF-Hands, causing a 'direct impact on Ca<sup>2+</sup> binding'. However, G113R lies outside a Ca<sup>2+</sup> coordinating loop and acts differently and more similar to the previously characterized arrhythmogenic N53I. Therefore, we suggest that altered apo/CaM dynamics may be a novel general disease mechanism, defining low-calcium target affinity - or Ca<sup>2+</sup> binding kinetics - critical for timely coordination of essential ion-channels in the excitation-contraction cycle. Calmodulin ; Allosteric regulation ; Mechanism (biology) ; Chemistry ; Dynamics (music) ; Biophysics ; Protein stability ; Biochemistry ; Biology ; Enzyme Enzyme Structure and Function ; Protein Structure and Dynamics ; Fungal and yeast genetics research