SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning.

Machine learning as a tool for chemical space exploration broadens horizons to work with known and unknown molecules. At its core lies molecular representation, an essential key to improve learning about structure-property relationships. Recently, contrastive frameworks have been showing impressive results for representation learning in diverse domains. Therefore, this paper proposes a contrastive framework that embraces multimodal molecular data. Specifically, our approach jointly trains a graph encoder and an encoder for the simplified molecular-input line-entry system (SMILES) string to perform the contrastive learning objective. Since SMILES is the basis of our method, i.e., we built the molecular graph from the SMILES, we call our framework as SMILES Contrastive Learning (SMICLR). When stacking a nonlinear regressor on the SMICLR's pretrained encoder and fine-tuning the entire model, we reduced the prediction error by, on average, 44% and 25% for the energetic and electronic properties of the QM9 data set, respectively, over the supervised baseline. We further improved our framework's performance when applying data augmentations in each molecular-input representation. Moreover, SMICLR demonstrated competitive representation learning results in an unsupervised setting.

Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining.

Ionic liquids have attracted the attention of researchers as possible electrolytes for electrochemical energy storage devices. However, their properties, such as the electrochemical stability window (ESW), ionic conductivity, and diffusivity, are influenced both by the chemical structures of cations and anions and by their combinations. Most studies in the literature focus on the understanding of common ionic liquids, and little effort has been made to find ways to improve our atomistic understanding of those systems. The goal of this paper is to explore the structural characteristics of cations and anions that form ionic liquids that can expand the HOMO/LUMO gap, a property directly linked to the ESW of the electrolyte. For that, we design a framework for randomly generating new ions by combining their fragments. Within this framework, we generate about 104 cations and 104 anions and fully optimize their structures using density functional theory. Our calculations show that aromatic cations are less stable ionic liquids than aliphatic ones, an expected result if chemical rationale is used. More importantly, we can improve the gap by adding electron-donating and electron-withdrawing functional groups to the cations and anions, respectively. The increase can be about 2 V, depending on the case. This improvement is reflected in a wider ESW.

Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition.

Most machine learning applications in quantum-chemistry (QC) data sets rely on a single statistical error parameter such as the mean square error (MSE) to evaluate their performance. However, this approach has limitations or can even yield incorrect interpretations. Here, we report a systematic investigation of the two components of the MSE, i.e., the bias and variance, using the QM9 data set. To this end, we experiment with three descriptors, namely (i) symmetry functions (SF, with two-body and three-body functions), (ii) many-body tensor representation (MBTR, with two- and three-body terms), and (iii) smooth overlap of atomic positions (SOAP), to evaluate the prediction process's performance using different numbers of molecules in training samples and the effect of bias and variance on the final MSE. Overall, low sample sizes are related to higher MSE. Moreover, the bias component strongly influences the larger MSEs. Furthermore, there is little agreement among molecules with higher errors (outliers) across different descriptors. However, there is a high prevalence among the outliers intersection set and the convex hull volume of geometric coordinates (VCH). According to the obtained results with the distribution of MSE (and its components bias and variance) and the appearance of outliers, it is suggested to use ensembles of models with a low bias to minimize the MSE, more specifically when using a small number of molecules in the training set.

Fluorimetric Detection of Zn2+, Mg2+, and Fe2+ with 3-Hydroxy-4-Pyridylisoquinoline as Fluorescent Probe.

The suitability of 3-hydroxy-4-pyridylisoquinoline to operate as fluorescent chemosensor for the detection of metal ions was investigated. For that purpose, the interactions of the title compound with selected metal ions were investigated by absorption and emission spectroscopy. The complexation of Zn2+, Fe2+, Mg2+ with 1:1 and 2:1 stoichiometry leads to characteristic optical responses that depend significantly on the employed solvents, thus allowing for the fluorimetric identification and detection of particular metal cations in a matrix-based pattern analysis or by fluorimetric titrations.

Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset.

Machine learning (ML) models can potentially accelerate the discovery of tailored materials by learning a function that maps chemical compounds into their respective target properties. In this realm, a crucial step is encoding the molecular systems into the ML model, in which the molecular representation plays a crucial role. Most of the representations are based on the use of atomic coordinates (structure); however, it can increase ML training and predictions' computational cost. Herein, we investigate the impact of choosing free-coordinate descriptors based on the Simplified Molecular Input Line Entry System (SMILES) representation, which can substantially reduce the ML predictions' computational cost. Therefore, we evaluate a feed-forward neural network (FNN) model's prediction performance over five feature selection methods and nine ground-state properties (including energetic, electronic, and thermodynamic properties) from a public data set composed of ∼130k organic molecules. Our best results reached a mean absolute error, close to chemical accuracy, of ∼0.05 eV for the atomization energies (internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, and free energy at 298.15 K). Moreover, for the atomization energies, the results obtained an out-of-sample error nine times less than the same FNN model trained with the Coulomb matrix, a traditional coordinate-based descriptor. Furthermore, our results showed how limited the model's accuracy is by employing such low computational cost representation that carries less information about the molecular structure than the most state-of-the-art methods.

Mild Synthesis of Fluorosolvatochromic and Acidochromic 3-Hydroxy-4-pyridylisoquinoline Derivatives from Easily Available Substrates.

The reaction of sodium cyanate with benzo[ b]quinolizinium substrates at room temperature gave 3-hydroxy-4-pyridyl-isoquinoline derivatives in good yields. Presumably, the overall reaction proceeds through an ANRORC-type sequence, that is, addition of the nucleophile, ring opening, and ring closure. Preliminary photophysical investigation of the parent compound revealed a pronounced sensitivity of its emission properties toward solvent effects and the pH of the medium.

Epidemiological profile and genetic resistance of Neisseria gonorrhoeae infection in women in a poor region of São Paulo, Brazil.

BACKGROUND AND AIM: Gonorrhea is a bacterial infection in the urogenital tract, transmitted by sexual or perinatal contact, caused by Neisseria gonorrhoeae, a gram-negative diplococcus. The present study evaluates the frequency of N. gonorrhoeae in women treated at Hospital Wladimir Arruda in poor area of São Paulo and also verifies the presence of genetic resistance against three antimicrobials of different classes: Tetracycline, Azithromycin and Ciprofloxacin. METHODS: This is an observational and descriptive study with a quantitative approach. Samples were collected at Hospital Escola Wladimir Arruda. The volunteers are women from 16 to 65 years of age. Sociodemographic, gynecological, sexual and health data are collected through a questionnaire, their symptoms/clinical manifestation were requested by the medical records, and then the participant is referred for collection of samples of cervical vaginal smear. The samples were screened for N. gonorrhoeae (dcmH gene) and tested for resistance genes to Tetracycline, Azithromycin and Ciprofloxacin through PCR. RESULTS: In the total of 127 samples analyzed by Real-Time PCR, 23 were positive and correspond to a general prevalence of a gonococcal infection in the studied population of 17% (CI:95%), and the participants were married (43.4%), had active sexual life (56.5%) and did not use any type of condom during sexual intercourse (52.1%). The resistance to the tetM ribosomal gene was found in 14 samples, prevalence of 60% (CI= 95%). CONCLUSIONS: We have described a concerning frequency of N. gonorrhoeae infection in females attended in an outcare patient. Also, most of the strains detected presented resistance to one or more antimicrobials.

Can in vitro embryo production be estimated from semen variables in Senepol breed by using artificial intelligence?

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.

Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation.

Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.

α-Phellandrene, a cyclic monoterpene, attenuates inflammatory response through neutrophil migration inhibition and mast cell degranulation.

AIMS: We aimed to investigate the modulating effect of α-phellandrene on neutrophil migration and mast cell degranulation processes. MAIN METHODS: Male Wistar rats or Swiss mice were treated p.o. with vehicle (3% Tween 80, p.o.), α-phellandrene (50, 100, or 200mg/kg, p.o.), or dexamethasone (0.5mg/kg, p.o.) 1h before carrageenan injection. Then, the neutrophil migration in 6-day-old air pouches or peritoneal cavities. The leukocyte rolling and adhesion were measured in real time and assessed by intravital microscopy. ELISA was used to detect TNF-α and IL-6 in peritoneal lavage. Compound 48/80-induced mast cell degranulation was assessed in mesenteric rat tissues. KEY FINDINGS: In all the tested doses, α-phellandrene prevented carrageenan-induced neutrophil accumulation (P<0.05). As detected by intravital microscopy, α-phellandrene also inhibited leukocyte rolling and adhesion, as well as significantly inhibited the production of the pro-inflammatory cytokines TNF-α and IL-6. Moreover, the degranulation of compound 48/80-induced mast cells was also inhibited by α-phellandrene (P<0.001). SIGNIFICANCE: These results suggest that α-phellandrene plays an important role as an anti-inflammatory agent through neutrophil migration modulation and mast cell stabilization.

Prevalence of facial trauma in contact sports practitioners in the Federal District of Brazil

Abstract Aims: The purpose of this study is to assess the prevalence of oral and dentoalveolar trauma among contact sports practitioners in the Federal District of Brazil. Methods: A cross-sectional descriptive study was conducted using a questionnaire developed specifically for this research regarding the occurrence of facial trauma, site of injuries, how they occurred, the approach is taken to solve the problem, and the use of several types of mouthguards. Data were analyzed using the SPSS 20.0 software, and the chi-square test (X2) was chosen to examine the differences between categorical variables. The results were considered statistically significant for p<0.05. Results: A total of 141 athletes were interviewed, with a prevalence of facial trauma of 65.2%, which was higher in professional athletes (71.1%). Lesions ranged from soft tissue lacerations to combined trauma; and the most frequent injuries were soft tissue laceration (53.3%), combined trauma (16.3%), and dental fracture (9.8%). Only 20.6% of the participants required treatment for related injuries. Regarding the use of mouthguards, 34% of the athletes reported regular use of this device, and Type II mouthguard was the most used (39.7%). Dentists participate in the process of production and dissemination of mouthguards in 17.1% and 10.5% of cases, respectively. Conclusion: The data showed that most athletes are not aware of the importance of using mouthguards. The dentist must be more present in the area of sports dentistry, both for awareness and production of these devices, which support the safe practice of contact sports.