Common Descriptors Research Articles

Cochlear Implant Sound Quality.

The aims of this exploratory study were (a) to assess common terms used to describe cochlear implant (CI) sound quality by patients fit with conventional CIs and (b) to compare those descriptors to previously obtained acoustic matches to CI sound quality created by single-sided deaf (SSD) patients for their normal-hearing ear. CI patients fit with Advanced Bionics (AB; n = 89), Cochlear Corporation (n = 86), and MED-EL (n = 80) implants were the participants. The patients filled out a questionnaire about CI sound quality for two time points: For the time near activation (T1) from memory and at the time of filling out the questionnaire (T2). The mean CI experience at T2 for the three groups ranged from 4 to 8 years. The questionnaire was composed of 25 adjectives describing sound quality. For T1, the most commonly used descriptors were Computer-like, Treble-y, Metallic, and Mickey Mouse-like. A superordinate category of HiPitched (High Pitched) gathered significantly more responses from patients with shorter electrode arrays (AB and Cochlear) than patients with longer arrays (MED-EL). At T2, the most common descriptor was Clear and was chosen by approximately two thirds of the patients. The between-group differences in responses to items in the HiPitched category, present at T1, were absent at T2. The questionnaire data from conventional CI patients differs from previous sound matching data collected from SSD-CI patients. Alterations to the spectral composition of the signal are less salient to experienced conventional patients than to experienced SSD-CI patients. This is likely due to the absence, for conventional patients, of an exemplar in an NH ear against which to judge CI sound quality.

Abstract 4136277: The Lay Public Misconceives Characteristics of Acute Coronary Syndrome-Related Chest Pain

Introduction: The most common acute coronary syndrome (ACS) symptom is chest pain. Chest pain is an umbrella term more precisely described using words like pressure or tightness. Previous studies have not explored the lay public’s conceptions of ACS-related chest pain. Misconceptions about chest pain make it difficult for individuals to recognize this symptom if they were to experience it, delaying care seeking. Research Question: How does the lay public conceive of ACS-related chest pain? Methods: Participants from across the U.S. were recruited in May and June 2023. Participants completed an online survey including the Chest Pain Conception Questionnaire. Descriptive statistics were used to characterize conceptions of chest pain quality, timing, intensity, and distress. Conceptions of men and women were compared using the independent-samples t -test and Chi-square test. Results: Participants (n=597) were mostly women (n=355; 59.5%) with a mean age of 54.0 years (SD = 11.5). The sample was diverse: White (n=414, 69.2%), Black (n=101; 16.9%), Hispanic or Latina/o/e/x (n=96; 16.1%), and Asian (n=67; 11.2%). Participants often thought of pressure (78.9%), tightness (77.4%), squeezing (75.5%), gripping (72.9%), and heaviness (72.5%) as possible descriptors of ACS-related chest pain. They less often thought of discomfort (48.7%), fullness (16.8%), and indigestion (13.4%). Participants expected ACS-related chest pain to be “intense” and to bother them “a lot,” both with median scores of 4.0 on a 5-point Likert scale. Participants felt that ACS-related chest pain would “often” (median 4.0) occur with activity and “sometimes” (median 3.0) occur at rest. Women were more likely to rate specific descriptors of chest pain (i.e., pressure, tightness) and general descriptors of chest pain (i.e., discomfort, pain) as more likely ACS-related chest pain descriptors than men, (all ps ≤ .05). Women (n=58; 16.3%) were about twice as likely as men (n=22; 9.2%) to endorse indigestion as a possible descriptor of ACS-related chest pain, ( p = .013, OR = 0.52 [95% CI = 0.31-0.87]). Conclusions: As many as 1 in 5 participants did not endorse common descriptors such as pressure and tightness as potentially characterizing ACS-related chest pain. Most believed this symptom would be intense and bother them a lot. They also more often associated ACS-related chest pain with activity than with rest. Clinical and research interventions can target the misconceptions identified in this study.

Spanish Doctoral Dissertations on the Properties of Materials in Engineering

We present a bibliometric study of engineering doctoral dissertations focusing on the properties of materials. The study exhaustively examines the production of doctoral theses on material properties in the field of engineering at Spanish universities from 1977 to 2022. The main objective is to analyse the volume and temporal distribution of these theses, explore their production by authors and directors considering gender, and identify the most common descriptors in the research. Technological innovation and progress in engineering materials are essential for social welfare, economic competitiveness, and international cooperation, highlighting the significance of these studies. The results reveal a total of 508 valid theses after a meticulous filtering process, with a notable increase in production post-1998, particularly during 2011–2020. The study highlights the limited presence of women in both authorship and direction of these, indicating male dominance in the field. However, there is an increasing trend of female participation in these academic areas. This study offers detailed insights into the trends and composition of doctoral research in materials engineering in Spain, pointing to important gender dynamics and research themes.

“I don’t know the correct way to describe it”: neuropathic pain experiences among athletes with spinal cord injury

BackgroundChronic pain among athletes is often misinterpreted as tissue damage resulting from sport. While researchers have started to examine neuropathic pain among athletes with spinal cord injury (SCI), there is...

What Is Chalky? Investigating Consumer Language and Perception of Fine Particles in Beverages Containing Pea and Potato Starch.

Despite its importance as an undesirable food texture, the phenomenon of chalkiness remains understudied. Chalky sensations presumably arise from fine particulates found in foods, but semantic overlap with other common descriptors of small particles, like gritty or sandy, is unclear. Here, we compare the usage of Chalky with related descriptors, and determine the effect of particle size, concentration, and xanthan content on Chalky ratings in a model beverage. A 23 factorial design with starch particle size (D90 = 33.8 and 64.6 µm), starch concentrations (10 and 20% w/v), and xanthan content (0.075 and 0.15% w/v) was used. Participants' salivary flow rate was also assessed. A multi-sip taste test was performed where naïve consumers (n = 82; 39% men, 60% women; age range = 18-79 years) rated the intensity of Chalky, Powdery, Gritty, Sandy, Mouthdrying, and Residual mouthcoating at 0, 30, and 60 s after each of three consecutive sips. All attribute ratings were highly correlated, with Chalky, Powdery, and Residual Mouthcoating being more closely correlated with each other than Gritty or Sandy. Although Chalky was still reported 60 s after consumption, no evidence of build-up was found with repeated sips. A larger size and higher concentration increased Chalky ratings, with the low-salivary-flow group reporting greater ratings for Chalky relative to the high-flow group. Our results suggest consumer percepts of small particles are overlapping but not entirely redundant. This suggests researchers and product developers should carefully distinguish between these descriptors when trying to understand consumer perception of food products containing fine particles.

Waterpipe Tobacco Brands and Flavors Sold Online in the USA

Introduction Flavor additives are commonly used in combustible tobacco products to mask harshness and increase appeal. However, research on the availability of flavored waterpipe tobacco (WT) is lacking, yet is important to support implementation of policies. Methods We completed a comprehensive online search in 2020 to identify WT brands and flavors sold online in the USA. We conducted a descriptive content analysis categorizing flavors as explicit (i.e., clear taste/flavor) or concept (i.e., no clear taste/flavor); and coded for 23 flavor descriptors (e.g., fruit, mint/menthol, tobacco). Flavor names were double-coded and discrepancies were resolved by a third coder. Results We identified 66 WT brands with 118 product lines (i.e., sub-brands). We found 2953 flavors, including 1871 unique flavors. Brands averaged 45 flavors (range: 1–183). Almost three quarters (73.5%, n = 2171) used explicit flavor names and 26.5% (n = 782) used concept flavor names. Concept flavors varied widely, and included names such as 1001 Nights and California Dream. The most common flavor descriptors were fruit (54.1%) and mint/menthol (12.5%). Tobacco was rarely (0.2%) used as a flavor descriptor. Flavor descriptors also included location (10.7%), color (11.1%), candy (6.3%), cool/ice (5.3%), and alcohol (5.5%). Conclusions WT is available in 1871 unique flavors, likely contributing to product appeal and use. Like other tobacco products, fruit and mint/menthol are common flavors. Given the significant health consequences associated with WT smoking and the role of flavors in product use, regulatory action specifically targeting WT flavors is urgently needed.

CAN REFERRAL TO COMMUNITY-BASED EXERCISE PHYSIOLOGIST SERVICES REDUCE EMERGENCY DEPARTMENT BURDEN? A CHART AUDIT

INTRODUCTION & AIMS Despite the rapid growth of exercise physiology in Australia, its potential within the healthcare system remains underutilised. Healthcare system strain, characterised by increased emergency department (ED) waiting times and length-of-stay is exacerbated by preventable, non-urgent ED attendances. In 2022, musculoskeletal conditions constituted the majority of >800,000 non-urgent ED presentations. Community care and underutilised allied health services, such as exercise physiology, may offer better management options. This study aimed to examine characteristics of patients presenting to ED for musculoskeletal conditions, treatment advice provided on physical activity, and referral practices to community-based exercise physiology services. METHODS A retrospective chart audit (n=267) examined patient characteristics, referral patterns and treatment advice due to non-urgent musculoskeletal conditions at Eastern Health EDs. RESULTS Most (n=223, 84%) patients presented with conditions that had persisted for a mean (SD) duration of 9.3 (32.4) days. Conditions affecting the leg (n=92, 34%), lower back (n=59, 22%) and arm (n=44, 16%) were most common. Sharp pain was the most common descriptor of pain (n=33, 12%). Almost half (45%, n=119) had comorbid cardiovascular disease (n=59, 22%), metabolic conditions (n=31, 12%) or mental health issues (n=29, 11%). Treatment advice on physical activity was provided for 17% to patients with 12% advised to avoid physical activity (n=31, 12%) and 5% to stay active (n=12). Advice to return to (n=2, 1%) or take time off (n=47, 18%) work was provided for 19% (n=49) patients. No patients were referred to exercise physiology services. CONCLUSION Patients presenting to EDs for musculoskeletal conditions appeared suitable for referral to community-based exercise physiology services, yet no referrals were identified. Moreover, few patients received advice on whether to stay active or avoid physical activity. Strategies to increase external ED referral to exercise physiology are warranted to alleviate burden within this setting.

Application and Prospect of Machine Learning in the Field of Materials and Chemical Engineering

With the rapid development of computer science, machine learning has gradually penetrated into various research fields and led to the development of related fields to different degrees. At recent stage, machine learning (ML) algorithms have been able to extensively search for variable relationships from a large amount of observed data, thus helping people to discover more laws that were once unnoticed. As an emerging approach, ML can efficiently utilize and process large-volume data generated from high-throughput experiments in the field of materials and chemicals. In recent years, with the continuous improvement of various algorithmic tools, more and more researchers have applied ML to materials and chemical research, which has greatly broadened the research ideas and methods. This paper briefly introduces the development of machine learning in recent years; summarizes the classification methods of common molecular descriptors and their types; introduces the classification, common algorithms and application scenarios of different types of ML; reviews the relevant research results in the field of materials prediction and inverse synthesis analysis of machine learning; puts forward related suggestions for the application of ML for research; and finally summarizes the prospects for the future development of machine learning applied to the field of materials and chemical engineering.

Reduction in the Sensor Effect on Acoustic Emission Data to Create a Generalizable Library by Data Merging.

The aim of this paper is to discuss the effect of the sensor on the acoustic emission (AE) signature and to develop a methodology to reduce the sensor effect. Pencil leads are broken on PMMA plates at different source-sensor distances, and the resulting waves are detected with different sensors. Several transducers, commonly used for acoustic emission measurements, are compared with regard to their ability to reproduce the characteristic shapes of plate waves. Their consequences for AE descriptors are discussed. Their different responses show why similar test specimens and test conditions can yield disparate results. This sensor effect will furthermore make the classification of different AE sources more difficult. In this context, a specific procedure is proposed to reduce the sensor effect and to propose an efficient selection of descriptors for data merging. Principal Component Analysis has demonstrated that using the Z-score normalized descriptor data in conjunction with the Krustal-Wallis test and identifying the outliers can help reduce the sensor effect. This procedure leads to the selection of a common descriptor set with the same distribution for all sensors. These descriptors can be merged to create a library. This result opens up new outlooks for the generalization of acoustic emission signature libraries. This aspect is a key point for the development of a database for machine learning.

Development and validation of a brain fog scale for coeliac disease.

Brain fog is a subjective cognitive impairment commonly reported in coeliac disease. A standardised tool to define and assess it is an important unmet need. To develop a patient-informed tool to assess brain fog in coeliac disease to support clinical care, research and drug development. A pilot online study defined patient descriptors of brain fog. A second study evaluated the factor structure and performance of the scale across two-time points ('Now' and in the 'Past week'). One month later, participants were invited to repeat the study with two online cognitive processing tests, the Stroop task and the trail making test. Among adults with treated coeliac disease, 37 (91.9% F) participated in the pilot study and 510 (88.8% F) in the second study of whom 99 repeated the study 1 month later with 51 completing cognitive testing. The most common brain fog descriptors were 'difficulty focusing', 'difficulty thinking' and 'difficulty finding the right words and communicating'. The 12-item scale reflects 'cognitive impairment' and 'somatic and affective experience' and demonstrates strong psychometric properties. It tracked with patients report of brain fog being present or absent across two-time points. It did not significantly correlate with the cognitive tests. The brain fog assessment and severity scale is the first patient-informed clinical outcomes assessment tool measuring brain fog in coeliac disease. It is brief and validated for two time-based formats. Further research coupling it with biomarker discovery is needed to confirm its validity as a predictor of cognitive performance.

How Do Patients and Otolaryngologists Define Dizziness?

To assess for differences in how patients and otolaryngologists define the term dizziness. Between June 2020 and December 2022, otolaryngology clinicians and consecutive patients at 5 academic otolaryngology institutions across the United States were asked to define the term "dizziness" by completing a semantics-based questionnaire containing 20 common descriptors of the term within 5 symptom domains (imbalance-related, lightheadedness-related, motion-related, vision-related, and pain-related). The primary outcome was differences between patient and clinician perceptions of dizzy-related symptoms. Secondary outcomes included differences among patient populations by geographic location. Responses were obtained from 271 patients and 31 otolaryngologists. Patients and otolaryngologists selected 7.7 ± 3.5 and 7.1 ± 4.3 symptoms, respectively. Most patients (266, 98.2%) selected from more than 1 domain and 17 (6.3%) patients identified symptoms from all 5 domains. Patients and clinicians were equally likely to define dizziness using terms from the imbalance (difference, -2.3%; 95% CI, -13.2%, 8.6%), lightheadedness (-14.1%; -29.2%, 1.0%), and motion-related (9.4; -0.3, 19.1) domains. Patients were more likely to include terms from the vision-related (23.6%; 10.5, 36.8) and pain-related (18.2%; 10.3%, 26.1%) domains. There were minor variations in how patients defined dizziness based on geographic location. Patients and otolaryngologists commonly described dizziness using symptoms related to imbalance, lightheadedness, and motion. Patients were more likely to use vision or pain-related terms. Understanding of these semantic differences may enable more effective patient-clinician communication.

Deep Learning-Based Chemical Similarity for Accelerated Organic Light-Emitting Diode Materials Discovery.

Thermally activated delayed fluorescence (TADF) material has attracted great attention as a promising metal-free organic light-emitting diode material with a high theoretical efficiency. To accelerate the discovery of novel TADF materials, computer-aided material design strategies have been developed. However, they have clear limitations due to the accessibility of only a few computationally tractable properties. Here, we propose TADF-likeness, a quantitative score to evaluate the TADF potential of molecules based on a data-driven concept of chemical similarity to existing TADF molecules. We used a deep autoencoder to characterize the common features of existing TADF molecules with common chemical descriptors. The score was highly correlated with the four essential electronic properties of TADF molecules and had a high success rate in large-scale virtual screening of millions of molecules to identify promising candidates at almost no cost, validating its feasibility for accelerating TADF discovery. The concept of TADF-likeness can be extended to other fields of materials discovery.

Artificial intelligence–adjudicated spatiotemporal dispersion: A patient-unique fingerprint of persistent atrial fibrillation

BackgroundSpatiotemporal dispersion-guided ablation is a tailored approach for patients in persistent atrial fibrillation (PsAF). The characterization of dispersion extent and distribution and its association with common clinical descriptors of PsAF patients has not been studied. ObjectivesArtificial intelligence–adjudicated dispersion extent and distribution (AI-DED) was obtained with a machine/deep learning classifier (VX1 Software, Volta Medical) in PsAF patients undergoing ablation. The purpose of this study was to test the hypothesis that AI-DED is unique to each patient and independent of common procedural and clinical parameters. MethodsIn a subanalysis of the Ev-AIFib study (NCT03434964), spatiotemporal dispersion maps were built with VX1 software in 78 consecutive persistent and long-standing PsAF patients. AI-DED was quantified using 2 distinct approaches (visual regional characterization or automated global quantification of AI-DED). ResultsAI-DED paired-subregion Euclidean distance measurements between 78 patients (average distance 5.07 ± 0.60; min 2.23; max 9.75) demonstrate that AI-DED is a patient-unique characteristic of PsAF. Importantly, both AF type and AF history do not correlate with AI-DED levels (R2 = 0.006, P = .53; and R2 = 0.03, P = .25, respectively). The most extensive AI-DED levels are not associated with poorer procedural (83%, 81%, and 83% of AF termination in low, medium, and high dispersion groups, respectively; P = .954) and long-term (88%, 75%, and 91% of freedom from AF/atrial tachycardia after multiple procedures; P = .517) outcomes. ConclusionThe atrial distribution and extent of multipolar electrogram spatiotemporal dispersion follow a nonrandom, albeit patient-unique, distribution in PsAF patients. AI-DED may represent a procedure-implementable fingerprint of the PsAF substrate.

Exploring human appreciation and perception of spontaneous urban fauna in Paris, France

City-dwellers are often confronted with the presence of many spontaneous animal species which they either like or dislike. Using a questionnaire, we assessed the appreciation and perception of the pigeon (Columba livia), the rat (Rattus norvegicus), and the hedgehog (Erinaceus europaeus) by people in parks, train stations, tourist sites, community gardens, and cemeteries in Paris, France. Two hundred individuals were interviewed between May 2017 and March 2018. While factors such as age, gender, level of education or place or location of the survey did not appear to be decisive in analyzing the differential appreciation of these species by individuals, there was a clear difference in appreciation based on the species and the perceived usefulness of the animal, which is often poorly understood. The rat was disliked (with an average appreciation score of 2.2/10), and the hedgehog was liked (with an average appreciation score of 7.7/10). The case of the pigeon is more complex, with some people loving or hating it, and many others being indifferent (with an average appreciation score of 4.7/10). The most common descriptor associated with pigeons and rats is the word "dirty", while for for hedgehogs it is "cute". Like Driscoll (1995) who found a positive correlation between perceived usefulness an appreciation given to the species, we assume that providing information to the citizens about the ecosystem services provided by unappreciated urban species could improve their perception of these animals, thus enabling better coexistence.

Справочная информационная система FLR-Library для адаптивного лесовосстановления: кластерный анализ дескрипторов

The lack of uniform terminological and technological approaches to the procedure of adaptive reforestation suggests the justification of the principles of creating a reference information system (FLR-Library), taking into account the specifics of implementing a joint decomposed FLR algorithm to reduce its execution time and rational forecasting of future forest landscape restoration projects. Collected and analyzed more than 120 definitions for more than 30 descriptors, such as "forest", "forest landscapes", "adaptive reforestation", "concepts of adaptive reforestation", "reforestation", "afforestation", "stand", "forest plantation", "forest complex", "forest landscape", "type of forest" and others. Both distinctive features and common points in the definitions of different countries and authors of the same descriptor are found. Technically, an approach to clustering of the most common descriptors is implemented: "forest", "forest landscapes", "reforestation", "afforestation". To do this, for each descriptor, based on the hierarchical clustering module, the combination of a cluster of recalibrated distances was determined and a cluster diagram was constructed based on «variables» (criteria for the descriptors effectiveness) and «observations» (by countries containing a source of information with a descriptor). Common to all sources will be that adaptive reforestation implies adaptation to climate change, but then there are two options for development: 1) planting (sowing) breeds resistant to climate change; 2) import and adaptation of "southern" breeds to northern latitudes. In the future, based on the analysis of descriptors and the principle of decomposition, it is planned to develop an algorithm for the functioning of the FLR system.

Classical singers are also proficient in non-classical singing.

Classical singers train intensively for many years to achieve a high level of vocal control and specific sound characteristics. However, the actual span of singers' activities often includes venues other than opera halls and requires performing in styles outside their strict training (e.g., singing pop songs at weddings). We examine classical singers' ability to adjust their vocal productions to other styles, in relation with their formal training. Twenty-two highly trained female classical singers (aged from 22 to 45 years old; vocal training ranging from 4.5 to 27 years) performed six different melody excerpts a cappella in contrasting ways: as an opera aria, as a pop song and as a lullaby. All melodies were sung both with lyrics and with a /lu/ sound. All productions were acoustically analyzed in terms of seven common acoustic descriptors of voice/singing performances and perceptually evaluated by a total of 50 lay listeners (aged from 21 to 73 years old) who were asked to identify the intended singing style in a forced-choice lab experiment. Acoustic analyses of the 792 performances suggest distinct acoustic profiles, implying that singers were able to produce contrasting sounding performances. Furthermore, the high overall style recognition rate (78.5% Correct Responses, hence CR) confirmed singers' proficiency in performing in operatic style (86% CR) and their versatility when it comes to lullaby (80% CR) and pop performances (69% CR), albeit with occasional confusion between the latter two. Interestingly, different levels of competence among singers appeared, with versatility (as estimated based on correct recognition in pop/lullaby styles) ranging from 62 to 83% depending on the singer. Importantly, this variability was not linked to formal training per se. Our results indicate that classical singers are versatile, and prompt the need for further investigations to clarify the role of singers' broader professional and personal experiences in the development of this valuable ability.

Multifractal foundations of biomarker discovery for heart disease and stroke

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.

Estimation of ambient marine noise levels in New South Wales

Marine and offshore infrastructure is increasingly becoming more prominent in NSW resulting in a requirement to investigate the potential behavioral and physiological impacts to marine fauna during construction and operational phases. Typically, detailed modeling is undertaken to compare the predicted underwater noise levels against noise thresholds with limited consideration to the existing ambient noise levels specific to the site. Vessel movements are known to be the primary contributor to ambient underwater noise levels from anthropogenic sources whereas prevailing noise is generally dominated by natural sources such as wind, waves, precipitation, and seismic activity. Underwater noise monitoring of baseline conditions is not always feasible and can also be cost-prohibitive to undertake. Additionally, there is currently limited publicly available underwater noise measurement data along the eastern coastline of NSW, specifically in shallow waters at depths less than 50 m. This study includes simple procedures to allow for preliminary estimates of the key contributors to the marine noise environment in the context of the Eastern coastline of NSW, with the aim of producing maps estimating existing marine noise levels for common descriptors such as SPL(rms) and SEL(24hour).

Evaluation of selected quantitative structure permeability relationship (QSPR) based mathematical models for the prediction of skin permeability of Camellia sinensis (tea) compounds

Background: Skin permeability coefficient (logKp) is a major determinant for topical drugs. In vitro and ex vivo determination of logKp is expensive, time and labour intensive, and difficult to apply to large databases. QSPR models derived from statistical correlation between descriptors of the compounds with the in vitro or ex vivo permeation data, are therefore used extensively. Vast number of QSPR equations makes the selection of a particular equation to screen a database, difficult choice. Objective: This study has evaluated common descriptor based equations for selection of the best suitable equation for screening phytochemical library of Camellia sinensis. Methods: Seven QSPR based models were used to estimate and compare the logKp of tea compounds. The best method was selected with respect to the gold standard logKp. Result: The model of Potts and Guy showed close proximation with the gold standard logKp and had highest association along with least RMSE value and least deviation. According to this method, approximately, 37.38%, 35.35% and 27.27% of the tea compounds were found to have high, good and poor logKp respectively. Conclusion: Potts and Guy equation can be effectively used to screen the phytochemical library of Camellia sinensis. This study has potential application in the field of topical medicine and cosmetics.

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.