jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marija Buljan | Peter Wick
Source: CHIMIA International Journal for Chemistry (article, 2022-03-30)
Field: Materials Science — Biomaterials

Tailoring Design of Nanomaterials and Systems to Individualize Patient Treatments Nanomedicine encompasses usage of materials smaller than 100 nm for diagnosis, monitoring and treatment of disease. A frequent application of these materials is in reformulation of active drugs, which were previously approved for clinical use. As illustrated with chemotherapeutics, delivery of a drug within a nanocarrier can represent a clear clinical benefit as it can increase its targeted uptake and reduce the off-target toxicity. Matching nanomedicine treatments with patient-specific biomarkers provides an exciting prospect for moving the filed towards precision medicine. In parallel, a strong potential for personalized treatments comes from employing nanomaterials for the delivery of patient-tailored biologically active molecules. Recent research and clinical data have highlighted mRNA and siRNA molecules, as well as short peptides, as powerful new drug classes that can be designed according to patient profiles and effectively delivered within nanoparticles. Particles used for therapeutic delivery are based on biodegradable and safe materials, frequently lipids and polymers, which can be further functionalized into more complex forms. Currently, there is a strong interest in developing specific nanocarrier formulations which can achieve optimal delivery of active molecules to targeted cells while reducing unwanted side-effects. Here, we discuss recent developments and future perspectives in the nanomedicine field and specifically highlight innovative approaches for the personalized patient treatments. Nanomedicine ; Nanocarriers ; Nanotechnology ; Drug delivery ; Personalized medicine ; Computer science ; Medicine ; Materials science ; Nanoparticle ; Bioinformatics Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: María A. Correa Deza | Constanza Belén Lobo | Marcela A. Ferrero | Marı́a Silvina Juárez Tomás
Source: Research in Microbiology (article, 2022-12-05)
Field: Materials Science — Biomaterials

Polyphosphate accumulation and cell-surface properties by autochthonous bacteria from Argentinian Patagonia Bacteria ; Polyphosphate ; Biology ; Rhodococcus ; Biofilm ; Microorganism ; Pseudomonas ; Microbiology ; Bacillus (shape) ; Food science ; Environmental chemistry ; Biochemistry ; Chemistry ; Phosphate biodegradable polymer synthesis and properties ; Chromium effects and bioremediation ; Wastewater Treatment and Nitrogen Removal
Authors: A. Ylinen | Laura Salusjärvi | Mervi Toivari | Merja Penttilä
Source: Metabolic Engineering Communications (article, 2022-04-30)
Field: Materials Science — Biomaterials

Control of D-lactic acid content in P(LA-3HB) copolymer in the yeast Saccharomyces cerevisiae using a synthetic gene expression system The fully biobased polyhydroxyalkanoate (PHA) polymers provide interesting alternatives for petrochemical derived plastic materials. The mechanical properties of some PHAs, including the common poly(3-hydroxybutyrate) (PHB), are limited, but tunable by addition of other monomers into the polymer chain. In this study we present a precise synthetic biology method to adjust lactate monomer fraction of a polymer by controlling the monomer formation <i>in vivo</i> at gene expression level, independent of cultivation conditions. We used the modified doxycycline-based Tet-On approach to adjust the expression of the stereospecific D-lactate dehydrogenase gene (<i>ldhA</i>) from <i>Leuconostoc mesenteroides</i> to control D-lactic acid formation in yeast <i>Saccharomyces cerevisiae</i>. The synthetic Tet-On transcription factor with a VP16 activation domain was continuously expressed and its binding to a synthetic promoter with eight transcription factor specific binding sites upstream of the <i>ldhA</i> gene was controlled with the doxycycline concentration in the media. The increase in doxycycline concentration correlated positively with <i>ldhA</i> expression, D-lactic acid production, poly(D-lactic acid) (PDLA) accumulation <i>in vivo</i>, and D-lactic acid content in the poly(D-lactate-<i>co</i>-3-hydroxybutyrate) P(LA-3HB) copolymer. We demonstrated that the D-lactic acid content of the P(LA-3HB) copolymer can be adjusted linearly from 6 mol% to 93 mol% <i>in vivo</i> in <i>S. cerevisiae</i>. These results highlight the power of controlling gene expression and monomer formation in the tuning of the polymer composition. In addition, we obtained 5.6% PDLA and 19% P(LA-3HB) of the cell dry weight (CDW), which are over two- and five-fold higher accumulation levels, respectively, than reported in the previous studies with yeast. We also compared two engineered PHA synthases and discovered that in <i>S. cerevisiae</i> the PHA synthase PhaC1437<sub>Ps6-19</sub> produced P(LA-3HB) copolymers with lower D-lactic acid content, but with higher molecular weight, in comparison to the PHA synthase PhaC1Pre. Lactic acid ; Biochemistry ; Saccharomyces cerevisiae ; Gene expression ; Polyhydroxyalkanoates ; Chemistry ; Yeast ; Biology ; Gene ; Bacteria biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Biopolymer Synthesis and Applications
Authors: Stephen Parlamas | Paul K. Goetze | Dillon Humpal | Dmitry Kurouski | Young‐Ki Jo
Source: Frontiers in Plant Science (article, 2022-06-15)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy Enables Confirmatory Diagnostics of Fusarium Wilt in Asymptomatic Banana <i>Fusarium oxysporum</i> f. sp. <i>cubense</i> (FOC) causes Fusarium wilt, one of the most concerning diseases in banana (<i>Musa</i> spp.), compromising global banana production. There are limited curative management options after FOC infections, and early Fusarium wilt symptoms are similar with other abiotic stress factors such as drought. Therefore, finding a reliable and timely form of early detection and proper diagnostics is critical for disease management for FOC. In this study, Portable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana. Banana plantlets were inoculated with FOC; uninoculated plants exposed to a drier condition were also prepared compared to well-watered uninoculated control plants. Subsequent Raman readings from the plant leaves, without damaging or destroying them, were performed weekly. The conditions of biotic and abiotic stresses on banana were modeled to examine and identify specific Raman spectra suitable for diagnosing FOC infection. Our results showed that Raman spectroscopy could be used to make highly accurate diagnostics of FOC at the asymptomatic stage. Based on specific Raman spectra at vibrational bands 1,155, 1,184, and 1,525 cm<sup>-1</sup>, Raman spectroscopy demonstrated nearly 100% accuracy of FOC diagnosis at 40 days after inoculation, differentiating FOC-infected plants from uninoculated plants that were well-watered or exposed to water deficit condition. This study first reported that Raman spectroscopy can be used as a rapid and non-destructive tool for banana Fusarium wilt diagnostics. Fusarium wilt ; Fusarium oxysporum ; Raman spectroscopy ; Horticulture ; Inoculation ; Asymptomatic ; Biology ; Medicine ; Physics ; Surgery ; Optics Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Essential Oils and Antimicrobial Activity
Authors: Puneet Pathak | Chakarvati Sango | Anshul Sabharwal | Rajeev Kumar Kapoor | Pratyoosh Shukla | Nishi Kant Bhardwaj
Source: Research Square (preprint, 2022-07-28)
Field: Materials Science — Biomaterials

Cellulase-assisted deinking approach for the recycling of mixed office waste papers and its quality improvement Deinking ; Cellulase ; Waste paper ; Quality (philosophy) ; Waste management ; Business ; Chemistry ; Engineering ; Enzyme ; Biochemistry ; Physics Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Recycling and Waste Management Techniques
Authors: Yuri Pereira Chuves | Midori Pitanga | Inga Grether | Maria Odila Hilário Cioffi | Francisco Maciel Monticeli
Source: Textiles (article, 2022-09-05)
Field: Materials Science — Polymers and Plastics

The Influence of Several Carbon Fiber Architecture on the Drapability Effect The growth of the aeronautical sector leads to the growth of polymer composites application, creating new demand for components applications in complex dimensions and shapes. Regarding different methods of draping 2D fabric into a 3D format, the concern is to keep the fabric properties and characteristics, since fiber orientation is modified after draping. For that purpose, this study aims to evaluate the drapability capacity of 2D dry fibrous fabrics (plain, twill, satin, non-crimp-fabric 0/90, and ±45) into a complex geometry, i.e., spherical indent. The energy required to drape fabric is composed of fabric deformation mechanisms (shear and bending), which were used together with microscopic deformation analysis to determine the appropriate fabric architectures with the highest malleability. Both NCF fabrics presented high energy and roughness on the fabric surface due to the folding effect of stitching. On the other hand, plain and twill weave fabrics required lower energy to drape but demonstrated higher fiber misalignment and deformation. The satin warp/weft relation favored shear and bending mechanisms, presenting better uniformity in load distribution, symmetry on drape capability, lower deformation degree, and lower fiber misalignment. Despite the intermediate load and energy required for drape, ANOVA and optimization methods confirmed that satin fabric showed better malleability behavior for complex geometries applications. Crimp ; Image stitching ; Composite material ; Materials science ; Deformation (meteorology) ; Bending ; Shear (geology) ; Plain weave ; Fiber ; Malleability ; Structural engineering ; Computer science ; Yarn ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Phuong Nguyen Thanh | Ming-Yuan Cho
Source: Computational Intelligence and Neuroscience (article, 2022-08-25)
Field: Materials Science — Materials Chemistry

Insulator Leakage Current Prediction Using Hybrid of Particle Swarm Optimization and Gene Algorithm-Based Neural Network and Surface Spark Discharge Data This study proposes a new superior hybrid algorithm, which is the particle swarm optimization (PSO) and gene algorithm (GA)-based neural network to predict the leakage current of insulators. The developed algorithm was utilized for the online monitoring systems, which were completely installed on the 69 kV and 161 kV transmission towers in Taiwan. This hybrid algorithm utilizes the local meteorological data as input parameters combined with the extracted enhanced data: the percentage of spark discharge areas and the brightness change in the image of the discharge phenomenon. These data with a high correlation with the leakage current are utilized as input vectors to improve the accuracy and effectiveness of the developed hybrid model. The performance of the developed algorithm is compared with a traditional PSO-based neural network and backpropagation neural network (BPNN) to evaluate and analyze. The comparative simulation results prove the effectiveness of the combination of hybrid PSO-GA-based neural network and surface discharge data, which achieved a maximum improvement of 38.54% MSE, 10.62% MAPE, and 3.41% <i>R</i> square for 161 kV data and 39.28% MSE, 12.62% MAPE, and 1.61% <i>R</i> square for 69 kV data. Moreover, the data with enhanced inputs outperform the traditional data in most benchmark factors, improving the accuracy and effectiveness in defining the deteriorative insulators. The developed methodology with a noticeable improvement was utilized in the online monitoring system to reduce the operational and maintenance cost of transmission lines in Taiwan Power Company. Particle swarm optimization ; Artificial neural network ; Algorithm ; Computer science ; Backpropagation ; Benchmark (surveying) ; SPARK (programming language) ; Mean squared error ; Machine learning ; Mathematics ; Statistics High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Water Systems and Optimization
Authors: Aminata Sow | Issiaka Traoré | Tidiane Diallo | Mohamed Traoré | Abdramane Ba
Source: Results in Chemistry (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Comparison of Gaussian process regression, partial least squares, random forest and support vector machines for a near infrared calibration of paracetamol samples In this article, we analyze the near-infrared (NIR) spectra of fifty-eight (58) commercial tablets of 500 mg of paracetamol from different origins (that is, with different batch numbers) in the local markets in Bamako. The NIR spectra were recorded in the spectral range 930 nm-1700 nm. The samples are divided into forty-eight (48) samples forming the set of calibration (training set) and ten (10) samples used as the validation or test set. To perform multivariate calibration, we apply three nonlinear regression techniques (Gaussian processes regression (GPR), Random Forest (RF), Support vector machine (KSVM)), along with the traditional linear partial least-squares regression (PLSR) to several data pretreatments of the 58 samples. The results show that the three nonlinear regression calibrations have better prediction performance than PLS as far as RMSE is concerned. To decide the best regression model, we avoid R2 since this quantity is not a good parameter for this purpose. We will instead consider RMSE when comparing the different multivariate models. Additionally, to assess the impact of data preprocessing, we apply the above regression techniques to the original data, Multi-scattering correction (MSC), standard variate normalization (SNV) correction, smoothing correction, first derivative (FD), and second derivative correction (SD). The overall results reveal that Gaussian Processes Regression (GPR) applied to smooth correction gives the lowest RMSEP=2.303053e-06 for validation (prediction) and RMSEC=2.112316e-06 for calibration. In our investigation, one also notices that the developed GPR model is more accurate and exhibits enhanced behavior no matter which data preprocessing is used. All in all, GPR can be seen as an alternative powerful regression tool for NIR spectra of paracetamol samples. The statistical parameters of the proposed model are compared to the results of some other models reported in the literature. Partial least squares regression ; Calibration ; Kriging ; Statistics ; Random forest ; Smoothing ; Mean squared error ; Support vector machine ; Mathematics ; Regression ; Linear regression ; Nonlinear regression ; Regression analysis ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Nafis Khuriyati | Anggoro Cahyo Sukartiko | Ririn Nur Alfiani
Source: International Food Research Journal (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Non-destructive measurement of antioxidant activity and water content in chili powder (Capsicum annuum L.) using near-infrared spectroscopy Chili (Capsicum annuum L.) is a potential source of antioxidants due to its high vitamin and lutein contents. The information on antioxidant activity and water content of chili are needed by the chili-based industries which utilise chili's natural antioxidant content for various purposes in health, food, or herbal products. These information are often obtained through destructive testing which damages the tested materials, and also requires a relatively long analysis time. Therefore, the present work aimed to non-destructively measure the antioxidant activity and water content of chili powder using near-infrared spectroscopy (NIS) combined with partial least square (PLS) analysis. A total of 84 samples of chili powder from four different colour groups were used in the present work. Approximately 70% of the samples were used for calibration, while the rest were used for validation. Samples' reflectance data were acquired using NIRFlex N500 Fiber Optic Solids Cells at wavelengths of 1000 - 2500 nm. The antioxidants activity and water content were also analysed destructively for comparison/reference data. PLS was then performed on the spectrum and reference using the Unscrambler X version 11.0 software. There were also three pre-treatments used to improve the spectrum, i.e., multiplicative scatter correction, first derivative Savitzky Golay, and de-trending. The best calibration model was successfully obtained when the antioxidant activity spectrum was processed using the de-trending pre-treatment with six PLS factors. The coefficient of determination (R2) and residual predictive deviation (RPD) values for this were 0.83 and 2.60, respectively. Additionally, the best calibration model was successfully obtained when the water content spectrum was processed using the first derivative Savitzky Golay pre-treatment with five PLS factors. The R2 and RPD values for this were 0.96 and 2.25, respectively. Both models were observed to be accurate, and have the potential to be used for measuring the antioxidant activity and water content of chili powder samples. Food science ; Chemistry ; Antioxidant ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Fangfang Qu | Lei Lin | Pengcheng Nie | Zhengyan Xia
Source: International Journal of Molecular Sciences (article, 2022-09-07)
Field: Chemistry — Spectroscopy

High-Precision Automatic Identification of Fentanyl-Related Drugs by Terahertz Spectroscopy with Molecular Dynamics Simulation and Spectral Similarity Mapping Fentanyl is a potent opioid analgesic with high bioavailability. It is the leading cause of drug addiction and overdose death. To better control the abuse of fentanyl and its derivatives, it is crucial to develop rapid and sensitive detection methods. However, fentanyl-related substrates undergo similar molecular structures resulting in similar properties, which are difficult to be identified by conventional spectroscopic methods. In this work, a method for the automatic identification of 8 fentanyl-related substances with similar spectral characteristics was developed using terahertz (THz) spectroscopy coupled with density functional theory (DFT) and spectral similarity mapping (SSM). To characterize the THz fingerprints of these fentanyl-related samples more accurately, the method of baseline estimation and denoising with sparsity was performed before revealing the unique molecular dynamics of each substance by DFT. The SSM method was proposed to identify these fentanyl analogs based on weighted spectral cosine-cross similarity and fingerprint discrete Fréchet distance, generating a matching list by stepwise searching the entire spectral database. The top matched list returned the identification results of the target fentanyl analogs with accuracies of 94.48~99.33%. Results from this work provide algorithms' increased reliability, which serves as an artificial intelligence-based tool for high-precision fentanyl analysis in real-world samples. Fentanyl ; Similarity (geometry) ; Computer science ; Biological system ; Artificial intelligence ; Chemistry ; Medicine ; Anesthesia ; Biology Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Dicho Stratiev | Ivelina Shishkova | Mihail Ivanov | Rosen Dinkov | Georgi Argirov | Svetlin Vasilev | Dobromir Yordanov
Source: Applied Sciences (article, 2022-11-21)
Field: Chemistry — Spectroscopy

Validation of Diesel Fraction Content in Heavy Oils Measured by High Temperature Simulated Distillation and Physical Vacuum Distillation by Performance of Commercial Distillation Test and Process Simulation A gas chromatography high temperature simulation distillation (HTSD: ASTM D 7169), and physical vacuum distillation (ASTM D 1160) were employed to characterize H-Oil vacuum distillates, straight run vacuum distillates, and hydrotreated vacuum distillates with the aim to determine their content of diesel fraction and evaluate the possible higher extraction of diesel fraction from the heavy oils. The ASTM D 7169 reported about six times as high diesel fraction content in H-Oil heavy distillates as that reported by the ASTM D 1160 method. Performing a commercial distillation column test along with a simulation of the column operation using data of both ASTM methods and a software process simulator revealed that the HTSD is the more valid method for proper determination of the diesel fraction content in heavy oils. The software process simulation of the commercial distillation column operation suggests that the HTSD could be considered as a true boiling point distillation method for heavy oils. The separation of the diesel fraction from the H-Oil heavy distillates quantified by the HTSD could deliver oil refining profit improvement in the amount of six digits USD per year. Vacuum distillation ; Distillation ; Fraction (chemistry) ; Diesel fuel ; Continuous distillation ; Boiling point ; Fractional distillation ; Fractionating column ; Fuel oil ; Batch distillation ; Process engineering ; Waste management ; Chemistry ; Environmental science ; Materials science ; Chromatography ; Engineering ; Organic chemistry Analytical Chemistry and Chromatography ; Process Optimization and Integration ; Crystallization and Solubility Studies