Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstral-domain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank. As next step, we select the best features using the Fast Correlation-Based Filtering method. Since our dataset has 129 users, then as next step, we augmented the training data by randomly selecting a percentage of the training data and adding a small random Gaussian noise to the extracted features. We then train a radial basis SVM model using the Leave-One-Out (LOO) methodology. The experimental results show an average accuracy classification improvement ranging of 15%, and an accuracy classification improvement ranging from 4% to 34% compared with baseline (state of the art) for specific emotions such as depression, anxiety, stress, and typical emotional states

Estudio de la composición química del aceite esencial de orégano (Origanum vulgare spp.) de Tacna

Se analizó químicamente el aceite esencial de orégano (Origanum vulgare spp.) cultivado en la ciudad de Tacna (Perú). El análisis fisicoquímico determinó las siguientes características: color (L* 60,51, a* –2,83, b* 14,31), índice de refracción de 1,475; densidad específica de 0,9132, y solubilidad en alcohol de 75 ml. Asimismo, la composición química se evaluó mediante cromatografía de gases, acoplada a espectrometría de masas. El cromatograma mostró los siguientes porcentajes: l-4 terpineol, 26,56; timol, 18,80; g-terpineno, 11,77; 2-careno, 6,53; terpineol, 4,08; m-cimeno, 3,27; y carvacrol, 2,24, entre otros presentes en menor proporción

Innovación abierta en tecnología, diseño y dinámicas sociales

ITESO, A.C

Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challenges require new approaches for more accurate diagnosis. Therefore, this work adds spectral and cepstral handwriting features to the already-used temporal, kinematic and statistics handwriting features. First, we calculate temporal and kinematic features using displacement; statistic features (SF) using displacement, and horizontal and vertical displacement; spectral (SDF) and cepstral (CDF) using displacement, horizontal and vertical displacement and pressure. Since the employed dataset (PaHaW) contains only 37 PD patients and 38 healthy control subjects (HC), then as the second step, we augment the percentage of the smaller training set to equal the larger. Next, we augment both classes to increase the training patient’s data and added random Gaussian noise in all augmentations. Third, the most relevant features were selected using the modified fast correlation-based filtering method (mFCBF). Finally, autoML is employed to train and test more than ten plain and ensembled classifiers. Experimental results show that adding spectral and cepstral features to temporal, kinematics and statistics features highly improved classification accuracy to 98.57%. Our proposed model, with lower computational complexities, outperforms conventional state-of-the-art models for all tasks, which is 97.62%

Exceptional Larval Morphology of Nine Species of the Anastrepha mucronota Species Group (Diptera: Tephritidae)

Anastrepha is the most diverse and economically important genus of Tephritidae in the American tropics and subtropics. The striking morphology of the third instars of Anastrepha caballeroi Norrbom, Anastrepha crebra Stone, Anastrepha haplacantha Norrbom & Korytkowski, Anastrepha korytkowskii Norrbom, Anastrepha nolazcoae Norrbom & Korytkowski, and three newly discovered and as yet formally unnamed species (Anastrepha sp. Peru-82, Anastrepha sp. nr. protuberans, and Anastrepha sp. Sur-16), and the more typical morphology of Anastrepha aphelocentema Stone, are described using light and scanning electron microscopy. To contribute to a better understanding of the interspecific and intraspecific variation among species in the mucronota species group and facilitate phylogenetic studies, we integrate molecular and morphological techniques to confirm the identity and describe third instars. Larva-adult associations and the identification of described larvae were confirmed using DNA barcodes. We provide diagnostic characters to distinguish larvae among these nine species of the mucronota group and separate them from those of the 29 other Anastrepha species previously described. We introduce the vertical comb-like processes on the oral margin as a novel character, and the unusual character states, including position and shape of the preoral lobe, and dentate or fringed posterior margins of the oral ridges and accessory plates. Our comparative morphology concurs with most previously inferred phylogenetic relationships within the mucronota group

Impact of smoking status on health-related quality of life (HRQoL) in cancer survivors

IntroductionThe Health-Related Quality of Life (HRQoL) often declines among cancer survivors due to many factors. Some cancer patients who smoke before the cancer diagnosis continue this harmful habit, potentially contributing to a more significant decline in their HRQoL. Therefore, this study investigates the association between smoking status and HRQoL in cancer survivors.MethodsWe conducted a cross-sectional study utilizing self-reported cancer history from 39,578 participants of the Behavioral Risk Factor Surveillance System (BRFSS) database, leveraging 2016 and 2020 year questionaries. A multidimensional composite outcome was created to assess HRQoL, integrating four distinct dimensions - general health, mental health, physical health, and activity limitations. After accounting for the complex survey design, logistic regression models were used to analyze the association between smoking status and poor HRQoL, adjusting for demographic, socioeconomic, and health-related confounders.ResultsOur study found that, after adjusting for potential confounders, current smokers exhibited a significantly poorer HRQoL than never smokers (OR 1.65, 95%CI 1.40-1.93). Furthermore, former smokers showed a poorer HRQoL than never smokers; however, this association was not as strong as current smokers (OR 1.22, 95%CI 1.09-1.38).ConclusionOur findings highlight the adverse association of smoking with poor HRQoL in cancer survivors, underscoring the importance of healthcare professionals prioritizing smoking cessation and providing tailored interventions to support this goal

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer