Computation of molecular description of supramolecular Fuchsine model useful in medical data.

Supramolecular chemistry is a fascinating field that explores the interactions between molecules to create higher-order structures. In the case of the supramolecular chain of Fuchsine acid, which is a type of dye molecule, several chemical applications are possible. Fuchsine acid helps to make better medicine carriers that deliver drugs where they're needed in the body, making treatments more effective and reducing side effects. It also helps create smart materials like sensors and self-fixing plastics, which are useful in electronics, keeping our environment clean, and making new materials. In sensing and detection, the supramolecular chain of Fuchsine acid utilizes as a sensor or detector for specific analyzes. In drug delivery, the supramolecular chains of Fuchsine acid incorporated into drug delivery systems. In recent years, a common method is linking a graph to a chemical structure and using topological descriptors to study it. This technique is becoming increasingly important over time. Topological descriptors gives very useful information while studying the topology of chemical graph. In this paper, we have computed the 3D structure of supramolecular graph of Fuchsine acid. We have computed an explicit expressions of ABC index, GA index, General Randi c ´ index, first and second Zagreb index, hyper Zagreb index, H-index and F-index of supramolecular structure of Fushine acid.

Amino acids as stabilizers for lysozyme during the spray-drying process and storage.

Amino acids (AAs) have been used as excipients in protein formulations both in solid and liquid state products due to their stabilizing effect. However, the mechanisms by which they can stabilize a protein have not been fully elucidated yet. The purpose of this study was to investigate the effect of AAs with distinct physicochemical properties on the stability of a model protein (lysozyme, LZM) during the spray-drying process and subsequent storage. Molecular descriptor based multivariate data analysis was used to select distinct AAs from the group of 20 natural AAs. Then, LZM and the five selected AAs (1:1 wt ratio) were spray-dried (SD). The solid form, residual moisture content (RMC), hygroscopicity, morphology, secondary/tertiary structure and enzymatic activity of LZM were evaluated before and after storage under 40 °C/75 % RH for 30 days. Arginine (Arg), leucine (Leu), glycine (Gly), tryptophan (Trp), aspartic acid (Asp) were selected because of their distinct properties by using principal component analysis (PCA). The SD LZM powders containing Arg, Trp, or Asp were amorphous, while SD LZM powders containing Leu or Gly were crystalline. Recrystallization of Arg, Trp, Asp and polymorph transition of Gly were observed after the storage under accelerated conditions. The morphologies of the SD particles vary upon the different AAs formulated with LZM, implying different drying kinetics of the five model systems. A tertiary structural change of LZM was observed in the SD powder containing Arg, while a decrease in the enzymatic activity of LZM was observed in the powders containing Arg or Asp after the storage. This can be attributed to the extremely basic and acidic conditions that Arg and Asp create, respectively. This study suggests that when AAs are used as stabilizers instead of traditional disaccharides, not only do classic vitrification theory and water replacement theory play a role, but the microenvironmental pH conditions created by basic or acidic AAs in the starting solution or during the storage of solid matter are also crucial for the stability of SD protein products.

Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts.

The development of highly efficient catalysts for formaldehyde (HCHO) oxidation is of significant interest for the improvement of indoor air quality. Up to 400 works relating to the catalytic oxidation of HCHO have been published to date; however, their analysis for collective inference through conventional literature search is still a challenging task. A machine learning (ML) framework was presented to predict catalyst performance from experimental descriptors based on an HCHO oxidation catalysts database. MnOx, CeO2, Co3O4, TiO2, FeOx, ZrO2, Al2O3, SiO2, and carbon-based catalysts with different promoters were compiled from the literature. Notably, 20 descriptors including reaction catalyst composition, reaction conditions, and catalyst physical properties were collected for data mining (2263 data points). Furthermore, the eXtreme Gradient Boosting algorithm was employed, which successfully predicted the conversion efficiency of HCHO with an R-square value of 0.81. Shapley additive analysis suggested Pt/MnO2 and Ag/Ce-Co3O4 exhibited excellent catalytic performance of HCHO oxidation based on the analysis of the entire database. Validated by experimental tests and theoretical simulations, the key descriptor identified by ML, i.e., the first promoter, was further described as metal-support interactions. This study highlights ML as a useful tool for database establishment and the catalyst rational design strategy based on the importance of analysis between experimental descriptors and the performance of complex catalytic systems.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Graph theoretical descriptors differentiate d-Mannose isomers in the principal component proposed feature space: A computational approach.

The intricate nature of carbohydrates, particularly monosaccharides, stems from the existence of several chiral centers within their tertiary structures. Predicting and characterizing the molecular geometries and electrostatic landscapes of these substances is difficult due to their complex electrical properties. Moreover, these structures can display a substantial degree of conformational flexibility due to the presence of many rotatable bonds. Moreover, identifying and distinguishing between D and L enantiomers of monosaccharides presents a significant analytical obstacle since there is a need for empirically measurable properties that can distinguish them. This work uses Principal Component Analysis (PCA) to explore the chemical information included in 3D descriptors in order to comprehend the conformational space of d-Mannose stereoisomers. The isomers may be discriminated by utilizing 3D matrix-based indices, geometrical descriptors, and RDF descriptors. The isomers can be distinguished by descriptors, such as the Harary-like index from the reciprocal squared geometrical matrix (H_RG), Harary-like index from Coulomb matrix (H_Coulomb), Wiener-like index from Coulomb matrix (Wi_Coulomb), Wiener-like index from geometrical matrix (Wi_G), Graph energy from Coulomb matrix (SpAbs_Coulomb), Spectral absolute deviation from Coulomb matrix (SpAD_Coulomb), and Spectral positive sum from Coulomb matrix (SpPos_Coulomb). Among these descriptors, the first two, H_RG and H_Coulomb, perform the best in differentiation among the 3D-Matrix-Based Descriptors (3D-MBD) class. The results obtained from this study provide a significant chemical insight into the structural characteristics of the compounds inside the graph theoretical framework. These findings are likely to serve as the basis for developing new methods for analytical experiments.

Quantitative radiomics and qualitative LI-RADS imaging descriptors for non-invasive assessment of ß-catenin mutation status in hepatocellular carcinoma.

PURPOSE: Gain-of-function mutations in CTNNB1, gene encoding for ß-catenin, are observed in 25-30% of hepatocellular carcinomas (HCCs). Recent studies have shown ß-catenin activation to have distinct roles in HCC susceptibility to mTOR inhibitors and resistance to immunotherapy. Our goal was to develop and test a computational imaging-based model to non-invasively assess ß-catenin activation in HCC, since liver biopsies are often not done due to risk of complications. METHODS: This IRB-approved retrospective study included 134 subjects with pathologically proven HCC and available ß-catenin activation status, who also had either CT or MR imaging of the liver performed within 1 year of histological assessment. For qualitative descriptors, experienced radiologists assessed the presence of imaging features listed in LI-RADS v2018. For quantitative analysis, a single biopsy proven tumor underwent a 3D segmentation and radiomics features were extracted. We developed prediction models to assess the ß-catenin activation in HCC using both qualitative and quantitative descriptors. RESULTS: There were 41 cases (31%) with ß-catenin mutation and 93 cases (69%) without. The model's AUC was 0.70 (95% CI 0.60, 0.79) using radiomics features and 0.64 (0.52, 0.74; p = 0.468) using qualitative descriptors. However, when combined, the AUC increased to 0.88 (0.80, 0.92; p = 0.009). Among the LI-RADS descriptors, the presence of a nodule-in-nodule showed a significant association with ß-catenin mutations (p = 0.015). Additionally, 88 radiomics features exhibited a significant association (p < 0.05) with ß-catenin mutations. CONCLUSION: Combination of LI-RADS descriptors and CT/MRI-derived radiomics determine ß-catenin activation status in HCC with high confidence, making precision medicine a possibility.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India).

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

Texture based feature extraction using symbol patterns for facial expression recognition.

Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the performance of a system. In this regard, by drawing inspiration from the Swastik symbol, three texture based feature descriptors named Symbol Patterns (SP1, SP2 and SP3) have been proposed for facial feature extraction. SP1 generates one pattern value by comparing eight pixels within a 3×3 neighborhood, whereas, SP2 and SP3 generates two pattern values each by comparing twelve and sixteen pixels within a 5×5 neighborhood respectively. In this work, the proposed Symbol Patterns (SP) have been evaluated with natural, fibonacci, odd, prime, squares and binary weights for determining the optimal recognition accuracy. The proposed SP methods have been tested on MUG, TFEID, CK+, KDEF, FER2013 and FERG datasets and the results from the experimental analysis demonstrated an improvement in the recognition accuracy when compared to the existing FER methods.

Influence of molecular descriptors of plant volatilomics on fumigant action against the three major stored product beetle pests.

Plant volatilomics such as essential oils (EOs) and volatile phytochemicals (PCs) are known as potential natural sources for the development of biofumigants as an alternative to conventional fumigant pesticides. This present work was aimed to evaluate the fumigant toxic effect of five selected EOs (cinnamon, garlic, lemon, orange, and peppermint) and PCs (citronellol, limonene, linalool, piperitone, and terpineol) against the Callosobruchus maculatus, Sitophilus oryzae, and Tribolium castaneum adults. Furthermore, for the estimation of the relationship between molecular descriptors and fumigant toxicity of plant volatiles, quantitative structural activity relationship (QSAR) models were developed using principal component analysis and multiple linear regression. Amongst the tested EOs, garlic EO was found to be the most toxic fumigant. The PCs toxicity analysis revealed that terpineol, limonene, linalool, and piperitone as potential fumigants to C. maculatus (< 20 µL/L air of LC50), limonene and piperitone as potential fumigants to T. castaneum (14.35 and 154.11 µL/L air of LC50, respectively), and linalool and piperitone as potential fumigants to S. oryzae (192.27 and 69.10 µL/L air of LC50, respectively). QSAR analysis demonstrated the role of various molecular descriptors of EOs and PCs on the fumigant toxicity in insect pest species. In specific, dipole and Randic index influence the toxicity in C. maculatus, molecular weight and maximal projection area influence the toxicity in S. oryzae, and boiling point and Dreiding energy influence the toxicity in T. castaneum. The present findings may provide insight of a new strategy to select effective EOs and/or PCs against stored product insect pests.

A systematic review of participant descriptors reported in studies of adhesive small bowel obstruction.

AIM: Reporting of participant descriptors in studies of adhesive small bowel obstruction (ASBO) can help identify characteristics associated with favourable outcomes and allow comparison with other studies and real-world clinical populations. The aim was to identify the pattern of participant descriptors reported in studies assessing interventions for ASBO. METHOD: This systematic review was registered with PROSPERO (CRD42021281031) and reported in line with the PRISMA checklist. Systematic searches of Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) were undertaken to identify studies assessing operative and non-operative interventions for adults with ASBO. Studies were dual screened for inclusion. Descriptors were categorised into conceptual domains by the research team. RESULTS: Searches identified 2648 studies, of which 73 were included. A total of 156 unique descriptors were identified. On average, studies reported 12 descriptors. The most frequently reported descriptors were sex, age, SBO aetiology, history of abdominal surgery, BMI and ASA classification. The highest number of descriptors in a single study was 34, compared to the lowest number of descriptors which was one. Pathway factors were the least frequently described domain. Overall, 37 descriptors were reported in just one study. CONCLUSION: There is a lack of consistency in participant descriptors reported in studies of SBO. Furthermore, a significant proportion of the descriptors were used infrequently. This makes it challenging to assess whether study participants are representative of the wider population. Further work is required to develop a Core Descriptor Set to standardise the reporting of patient characteristics and reduce heterogeneity between studies.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors.

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Nickel-Catalyzed Three-Component Unsymmetrical Bis-Allylation of Alkynes with Alkenes: A Density Functional Theory Study.

Density functional theory (DFT) characterizations were employed to resolve the structural and energetic aspects and product selectivities along the mechanistic reaction paths of the nickel-catalyzed three-component unsymmetrical bis-allylation of alkynes with alkenes. Our putative mechanism initiated with the in situ generation of the active catalytic species [Ni(0)L2] (L = NHC) from its precursors [Ni(COD)2, NHC·HCl] to activate the alkyne and alkene substrates to form the final skipped trienes. This proceeds via the following five sequential steps: oxidative addition (OA), ß-F elimination, ring-opening complexation, C-B cleavage and reductive elimination (RE). Both the OA and RE steps (with respective free energy barriers of 24.2 and 24.8 kcal·mol-1) contribute to the observed reaction rates, with the former being the selectivity-controlling step of the entire chemical transformation. Electrophilic/nucleophilic properties of selected substrates were accurately predicted through dual descriptors (based on Hirshfeld charges), with the chemo- and regio-selectivities being reasonably predicted and explained. Further distortion/interaction and interaction region indicator (IRI) analyses for key stationary points along reaction profiles indicate that the participation of the third component olefin (allylboronate) and tBuOK additive played a crucial role in facilitating the reaction and regenerating the active catalyst, ensuring smooth formation of the skipped triene product under a favorably low dosage of the Ni(COD)2 catalyst (5 mol%).

Assessment of the application of selected metal-organic frameworks as advanced sorbents in passive extraction used in the monitoring of contaminants of emerging concern in surface waters.

Water pollution has become a critical global concern requiring effective monitoring techniques and robust protection strategies. Contaminants of emerging concern (CECs) are increasingly detected in various water sources, with their harmful effects on humans and ecosystems continually evolving. Based on literature reports highlighting the promising sorption properties of metal-organic frameworks (MOFs), the aim of this study was to evaluate the suitability of NH2-MIL-125 (Ti) and UiO-66 (Ce) as sorbents in passive sampling devices (MOFs-PSDs) for the collection and extraction of a wide group of CECs. Solvothermal methods were used to synthesize MOFs, and the characterization of the obtained materials was performed using field-emission scanning electron microscopy (FE-SEM), powder X-ray diffractometry (pXRD) and Fourier-transform infrared (FTIR) spectroscopy. The research demonstrated the sorption capabilities of the tested MOFs, the ease and rapidity of their chemical regeneration and the possibility of reuse as sorbents. Using chemometric analysis, the structural properties of CECs determining the sorption efficiency on the surface of NH2-MIL-125 (Ti) were identified. The MOFs-PSDs were lab-calibrated to examine the kinetics of analytes sorption and determine the sampling rates (Rs). MOFs-PSDs and CNTs-PSDs (PSDs containing carbon nanotubes as a sorbent) were then placed in the Elblag River and the Vistula Lagoon to sampling and extraction of the target compounds from the water. CNTs-PSDs were selected, based on our previous research, for the comparison of the effectiveness of the MOFs-PSDs in environmental monitoring. MOFs-PSDs were successfully used in monitoring of CECs in water. The time-weighted average concentrations (CTWA) of 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determined in the Elblag River and CTWA of metoprolol, diclofenac, 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determine in the Vistula Lagoon using MOFs-PSDs and a high-performance liquid chromatography coupled with triple quadrupole mass spectrometer.

Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves.

BACKGROUND: Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings. OBJECTIVE: This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV. METHODS: Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups. RESULTS: The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection. CONCLUSION: By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Hydrogen evolution descriptors of 55-atom PtNi nanoclusters and interaction with graphite.

Density functional simulations have been performed for PtnNi55-nclusters (n=0,12,20,28,42,55) to investigate their catalytic properties for the hydrogen evolution reaction (HER). Starting from the icosahedralPt12Ni43, hydrogen adsorption energetics and electronicd-band descriptors indicate HER activity comparable to that of purePt55(distorted reduced core structure). The PtNi clusters accommodate a large number of adsorbed hydrogen before reaching a saturated coverage, corresponding to 3-4 H atoms per icosahedron facet (in total ∼70-80). The differential adsorption free energies are well within the window of|ΔGH|<0.1 eV which is considered optimal for HER. The electronic descriptors show similarities with the platinumd-band, although the uncovered PtNi clusters are magnetic. Increasing hydrogen coverage suppresses magnetism and depletes electron density, resulting in expansion of the PtNi clusters. For a single H atom, the adsorption free energy varies between -0.32 (Pt12Ni43) and -0.59 eV (Pt55). The most stable adsorption site is Pt-Pt bridge for Pt-rich compositions and a hollow site surrounded by three Ni for Pt-poor compositions. A hydrogen molecule dissociates spontaneously on the Pt-rich clusters. The above HER activity predictions can be extended to PtNi on carbon support as the interaction with a graphite model structure (w/o vacancy defect) results in minor changes in the cluster properties only. The cluster-surface interaction is the strongest forPt55due to its large facing facet and associated van der Waals forces.

Work Function-Guided Electrocatalyst Design.

The development of high-performance electrocatalysts for energy conversion reactions is crucial for advancing global energy sustainability. The design of catalysts based on their electronic properties (e.g., work function) has gained significant attention recently. Although numerous reviews on electrocatalysis have been provided, no such reports on work function-guided electrocatalyst design are available. Herein, a comprehensive summary of the latest advancements in work function-guided electrocatalyst design for diverse electrochemical energy applications is provided. This includes the development of work function-based catalytic activity descriptors, and the design of both monolithic and heterostructural catalysts. The measurement of work function is first discussed and the applications of work function-based catalytic activity descriptors for various reactions are fully analyzed. Subsequently, the work function-regulated material-electrolyte interfacial electron transfer (IET) is employed for monolithic catalyst design, and methods for regulating the work function and optimizing the catalytic performance of catalysts are discussed. In addition, key strategies for tuning the work function-governed material-material IET in heterostructural catalyst design are examined. Finally, perspectives on work function determination, work function-based activity descriptors, and catalyst design are put forward to guide future research. This work paves the way to the work function-guided rational design of efficient electrocatalysts for sustainable energy applications.

Assessment of liquid-liquid partition for the assignment of descriptors for the solvation parameter model.

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and McGowan's characteristic volume, V, to model transfer properties between condensed phases. The V descriptor is assigned from structure. For compounds liquid at 20 °C the E descriptor can be assigned from the characteristic volume and its refractive index. The E descriptor for compounds solid at 20 °C and the S, A, and B descriptors are experimental properties traditionally assigned from chromatographic, liquid-liquid partition, and solubility measurements. In this report liquid-liquid partition constants in totally organic and aqueous biphasic systems are evaluated as a standalone technique for descriptor assignments. Using six totally organic biphasic systems the S, A, and B descriptors were assigned with an average absolute deviation (AAD) of about 0.04, 0.03, and 0.04, respectively, compared with the best estimate of the true descriptor values for 65 compounds. The E descriptor for compounds solid at 20 °C can only be estimated with an AAD of approximately 0.1. For six aqueous biphasic systems the B descriptor is assigned with a lower AAD of 0.028 and higher AAD of 0.08 and 0.05 for the S and A descriptors, respectively, than for the totally organic biphasic systems for compounds with a reliable value for the E descriptor. The preferred system for descriptor assignments utilizes both totally organic biphasic systems (heptane-1,1,1-trifluoroethanol, isopentyl ether-propylene carbonate, isopentyl ether-ethanolamine, heptane-ethylene glycol, heptane-formamide, and 1,2-dichloroethane-ethylene glycol) and aqueous biphasic systems (octanol-water, cyclohexane-water) with the possible substitution of some systems with alternative systems of similar selectivity. For 55 varied compounds this combination of eight organic and aqueous biphasic systems resulted in an AAD of approximately 0.03, 0.02, and 0.02 for the S, A, and B descriptors compared to the best estimate of the true descriptor value. For 30 compounds solid at 20 °C the AAD for the E descriptor of 0.11 is poorly assigned. The relative average absolute deviation in percent (RAAD) corresponds to 9.7 %, 3.1 %. 4.0 % and 8.3 % for E, S, A, and B, respectively, for the eight biphasic systems. Liquid-liquid partition is compared to reversed-phase liquid and gas chromatography as a standalone technique for descriptor assignments.

A Richer Vocabulary of Chinese Personality Traits: Leveraging Word Embedding Technology for Mining Personality Descriptors.

This study uses a data-driven approach to mine the distribution of personality traits among Chinese people in the Chinese social context. Based on the hypothesis of personality lexicology, word embedding technology was employed in machine learning to mine personality vocabulary from Tencent's word embedding database. More than 10,000 Chinese personality descriptors were extracted and analyzed using Gaussian Mixture Model Cluster and Hierarchical clustering analysis. The data was collected from 658 Chinese people randomly from all parts of China through an online questionnaire method. The results reveal six personality traits in the Chinese context, expanding the personality thesaurus and providing examples to illustrate each trait. The findings coincide with previous research on the five-factor model, which partially describes the personality traits of Chinese people, but does not offer a complete explanation of their typical social behavior patterns. Additionally, the study supports the notion of cultural particularity in personality traits. The approach used in this study offers a richer personality vocabulary than traditional personality mining methods, and word embedding technology captures richer semantic information in Chinese. The six Chinese personality traits identified in this study will also be used to explore how to quantify and evaluate personality traits based on word embedding and personality descriptors.

Local Diagnostic Reference Levels for Adult Computed Tomography Urography Exams.

A Computed Tomography Urography (CTU) scan is a medical imaging test that examines the urinary tract, including the bladder, kidneys, and ureters. It helps diagnose various urinary tract diseases with precision. However, patients undergoing CTU imaging receive a relatively high dose of radiation, which can be a concern. In our research paper, we analyzed the Computed Tomography Dose Index (CTDIvol) and Dose-Length Product (DLP) for 203 adult patients who underwent CTU at one of the most important regional centers in Bosnia and Herzegovina that sees a large number of patients. Our study included the distribution of age and sex, the number of phases within one examination, and different clinical indications. We compared our findings with the results available in the scientific literature, particularly the recently published results from 20 European countries. Furthermore, we established the local diagnostic reference levels (LDRLs) that can help set the national diagnostic reference levels (NDRLs). We believe our research is a significant step towards optimizing the protocols used in different hospitals in our country.

Machine Learning to Predict Enzyme-Substrate Interactions in Elucidation of Synthesis Pathways: A Review.

Enzyme-substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.