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

Estudio de las principales características fisicoquímicas y comportamiento del Sanqui (Corryocactus brevistylus subsp. puquiensis (Rauh & Backeberg) Ostolaza) en almacenamiento

El presente trabajo de investigación, permitió caracterizar físicoquímicamente al sanqui; y determinar su comportamiento en almacenamiento. La pulpa de sanqui presentó la siguiente composición fisicoquímica: humedad 95.2%, proteínas 1.3%, grasa 0.0%, carbohidratos 3.1%, fibra 0.9%, cenizas 0.4%, vitamina C 57.1 mg/100 gr., capacidad antioxidante 474.8 ug. de eq. Trolox/gr., calcio 104.5 ppm, potasio 5566.4 ppm, fósforo 128 ppm, magnesio 145 ppm, acidez 2.3%, pH 2.7, ºBrix 2.9; y la cáscara: humedad 91.6%, proteína 1.4%, grasa 0.0%, carbohidratos 5.6%, fibra 1.7%, cenizas 1.4%, Vitamina C 2.5 mg/100 gr. de fruta, calcio 752 ppm, potasio 1743.9 ppm, fósforo 67 ppm y acidez 0.54%. Se determinó que están presentes las siguientes sustancias fitoquímicas en pulpa: azúcares reductores, lactonas, triterpenos-esteroides, antocianidinas y mucílagos; y en cáscara: azúcares reductores, triterpenos-esteroides y catequinas. De las tres temperaturas de almacenamiento evaluadas: 6ºC, 12ºC y 18°C, se determinó que el sanqui se conserva mejor a 6°C y 90% de H.R

Obtención de un filtrante de maíz morado (Zea mays L.), evaluación de pérdida de color y degradación de antocianinas en el almacenaje

El trabajo de investigación permitió obtener un filtrante de maíz morado, evaluar la pérdida del color y degradación de las antocianinas durante el almacenaje a 70, 80, 90 y 100% de humedad relativa (H.R.). Se obtuvo el filtrante de maíz morado mediante una mezcla de canela 1%, clavo de olor 1%, grano 10% y coronta de maíz morado 88%, con una tamaño de partícula entre 2 y 4mm. La caracterización del filtrante reportó: humedad 7.9%, carbohidratos 74.4%, cenizas 2.4%, grasa 0.2%, proteína 5.1%, fibra 10.0%, actividad de agua 0.48; y coordenadas de color L*= 40.26, a*=7.96 y b*= 0.82. Las pruebas estadísticas reportaron mayor estabilidad en: color y antocianinas monoméricas en los filtrantes almacenados en ambientes a 70 y 80% de humedad relativa (H.R.). La cinética de degradación fue de primer orden con velocidades de reacción de 0.0126, 0.0149, 0.0283 y 0.0291 días-1, durante el almacenaje a 70, 80, 90 y 100% de humedad relativa (H.R.), respectivamente. El tiempo de vida media fue de 55, 46.5, 24.5 y 23.8 días a 70, 80, 90 y 100% de humedad relativa (H.R.), respectivamente en empaques de polipropileno con permeabilidad de 0.705 g-milipulgadas/ m2-día-mmHg

Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively

A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa