Inflammatory Potential of Diet And Pancreatic Cancer Risk: Interaction And Mediation Analysis In Two Prospective Cohorts

Background: Inflammation plays a pivotal role in pancreatic cancer etiology and can be modulated by diet. We aimed to examine the association between inflammatory potential of diet, assessed with the Dietary Inflammatory Index (DIITM), and pancreatic cancer risk in two prospective cohorts, the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and the National Institutes of Health American Association of Retired Persons (NIH-AARP) Diet and Health Study. We explored effect modification by important inflammation-related lifestyle factors, and investigated whether type-2 diabetes mediated the association in a pooled analysis of both studies. Methods: A total of 101,449 and 533,286 participants aged between 50 to 78 years at baseline were included in the analytical cohort of PLCO and NIH-AARP, respectively. Energy-adjusted DII (E-DII) scores were computed based on food and supplement intake. Multivariable-adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pancreatic cancer by E-DII quintiles with participants in the lowest E-DII quintile (most anti-inflammatory scores) as the referent. We estimated natural direct effect, natural indirect effect, and marginal total effect of both categorical and continuous E-DII scores on pancreatic cancer with type-2 diabetes as a mediator using causal mediation approach. Results: A total of 328 and 3,338 pancreatic cancer cases were identified in the PLCO and NIH-AARP, respectively. There was no significant association between dietary inflammatory potential and pancreatic cancer risk in either the PLCO or NIH-AARP. However, time significantly modified the association in PLCO (P-interaction=0.02). An inverse association in the first four years of follow up was observed (HRQ5vsQ1=0.55; 95% CI=0.32-0.95; P-trend=0.15), while there was a positive trend among those with ≥4 years of follow-up (HRQ5vsQ1 =1.36; 95% CI=0.85-2.17; P-trend=0.03). Type-2 diabetes significantly mediated the EDII and pancreatic cancer association (P\u3c0.05). Conclusion: Findings from these two large prospective cohorts did not support the association between inflammatory potential of diet and pancreatic cancer risk. Reverse causality owing to undetected disease may account for the inverse association observed in the first four years of follow-up in the PLCO. Type-2 diabetes explained an underlying mechanism through dietary inflammatory potential to pancreatic cancer development

A Strategic Analysis of Algorithm Manipulation: a Lending Game perspective

Machine learning models are widely used in many business contexts, but there is a growing concern that strategic individuals may manipulate their features to obtain desirable outcomes from the machine learning models. This paper offers a theoretical analysis of the impact of feature manipulation on the performance of the machine learning models and the payoffs of firms in an online lending context. Contrary to the common belief, our interesting finding is that manipulation may not be harmful to a firm under some circumstances. Instead, it could increase the classification model\u27s performance and raise a firm\u27s payoff and the social welfare when high-quality individuals manipulate more. Overall, our findings suggest that manipulation can bring strategic value to machine learning models instead of just being a harmful activity. Our findings provide useful insights for feature engineering and lay a foundation for future research about optimal strategies to cope with manipulation activities

Pedestrian detection in real time using Deep Learning algorithms

Trabajo de Fin de Máster en Internet de las Cosas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021En muchas escenas existe la necesidad de contar el número de personas que acceden a un determinado espacio para limitar y controlar su flujo, especialmente en los tiempos de pandemia. Por lo tanto, en el presente proyecto se presenta una posible solución para contar las personas de forma automática en tiempo real, utilizando la tecnología del aprendizaje profundo. La solución, enmarcada bajo el paradigma de Internet de las Cosas (Internet of Things, IoT) consta principalmente de dos partes, nodo IoT y servidor. El nodo IoT se encarga de capturar las imágenes mediante un módulo de cámara y las envía al servidor. El servidor es un dispositivo con altas capacidades computacionales, que se encarga de los procesamientos de las imágenes, detectando las personas que aparecen en dichas imágenes mediante el uso de algoritmos de aprendizaje profundo. En el presente proyecto se han utilizado dos algoritmos, Mask R-CNN y YOLACT, con tal propósito, que permiten analizar los diferentes resultados. Conjuntamente con ellos, se ha utilizado el algoritmo de DeepSORT para realizar el seguimiento de objetos, asignando un ID a las personas detectadas. Finalmente, con las coordenadas obtenidas de las personas, se determina su sentido de movimiento y se alerta cuando el número de personas supera el límite establecido en el espacio objeto de monitorización. Todos estos métodos y funcionalidades se han integrado convenientemente hasta lograr una solución conceptual IoT, que ha permitido comparar y evaluar las distintas estrategias integradas.In many scenes, there is a need to determine the number of people to limit and control their flow, especially in times of pandemic. Therefore, this project presents a possible solution to count people automatically in real time, using deep learning technology. The solution, consists, mainly of two parts, IoT node and server. The IoT node is responsible for capturing images using a camera module and sends them to the server. The server is a high computational device that is in charge of image processing, detecting the people in the images using deep learning algorithms. In the project, two algorithms, Mask R-CNN and YOLACT, have been used to analyse different results. Then, the DeepSORT algorithm has been used to track objects, assigning an ID to the detected people. Finally, with the coordinates obtained from the people, their direction of movement is determined and an alert is given when the number of people exceeds the limit. All these methods and functions are conveniently integrated to achieve a conceptual solution IoT, which has allowed to compare and evaluate the different integrated strategiesDepto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Análisis comparativo de los sistemas tributarios de España y China

El objeto de este trabajo es conocer y analizar comparativamente, las características principales de los sistemas tributarios de España y de la República Popular China. En la primera parte del trabajo se hace una breve aproximación a los dos países, presentando las magnitudes básicas relacionadas con el objeto del mismo. Seguidamente se expone el motivo por el que se requiere un sistema tributario y se analizan los gastos e ingresos públicos, que suponen sus dos principales componentes. A continuación, se presentan los aspectos generales de los dos sistemas tributarios, con el propósito de identificar la estructura básica de los mismos, facilitando el estudio de su razón principal, que no es otra que la administración íntegra de los tributos, para después comparar el IRPF e IVA de ambos sistemas, finalizando el trabajo con las conclusiones que se derivan de los datos e informaciones investigados y procesados.The purpose of this work is to understand and compare the main features of the tax systems of Spain and the People´s Republic of China. The first part briefly introduces the situation of these two countries. Next, explains why a tax system is needed, and analyzes its two main components - public expenditures and revenues -. The following is an overview of the overall situation of these two tax systems, with the purpose of determining their basic structure, so as to facilitate the study of comprehensive administration of taxes. Then, the Individual Income Tax and VAT are compared, finally, the conclusions are drawn according to the data researched and processed

High School Students’ Math and Science Motivation Profiles: Stability and Relationship With Their Stem Career Aspirations

Motivation to study mathematics and science is an important influencing factor of career aspirations in STEM fields which predicts STEM major choice in college and STEM careers after graduation. Using restricted data from a nationally representative sample HSLS:09, the current study identified U.S. high school students’ motivation profiles in mathematics and science courses in 9th and 11th grade, examined the stability of these profiles across the two time points, and studied the association between 11th grade motivation profiles and STEM career aspirations. Differences between male and female students in motivation profiles, profile stability and career aspirations were examined. The stability of STEM career aspirations between 9th grade and 11th grade and the consistency between 11th grade STEM career aspirations and STEM major choice in college were also investigated. Latent profile analysis revealed four distinct motivation profiles at both time points. Latent transition analysis found substantial stability in profiles: participants were most likely to stay in their original profiles than transition to another profile. Students in the High All profile in 11th grade were more likely to aspire for STEM careers and health occupations than those in other profiles. Students in the Higher Science profile were more likely to aspire for health occupations than those in the Higher Math profile. There were significant differences between male and female students in profile membership, transition probability, and STEM career aspirations. In general, male students were more likely to be in latent profiles characterized by higher math and science motivation and aspire for traditional STEM careers. Female students were more likely to be in profiles characterized by lower motivation and aspire for health occupations. Career aspirations remained relatively stable from 9th grade to 11th grade. About 70% of students had the same career aspirations in 11th grade as in 9th grade. About 62.5 % of the participants’ first major in college was consistent with their career aspirations in 11th grade. Implications of these results for research and interventions on math and science motivation and STEM career aspirations are discussed