Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Quality assessment and validation of digital PCR (dPCR) for grapevine virus diagnosis(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Hernández Pérez, Daniella María Joselyn; Díaz Lara, Alfredo; emipsanchez; Carrillo Tripp, Jimena; Rodríguez García, Manuel; School of Engineering and Sciences; Campus MonterreyGrapevine is a highly economically important crop in Mexico. However, it can be affected by several pathogens, including viruses that can cause significant crop losses. It is important to identify early the infected plants to manage the disease correctly and prevent economic losses. Traditional detection methods have drawbacks, such as limited sensibility and accuracy. Digital PCR (dPCR) is an innovative method that claims to be more sensitive and reproducible than the routine method for virus identification: quantitative PCR (qPCR). This study assesses reverse transcription dPCR (RT-dPCR) as a method for the detection and quantification of RNA grapevine viruses focusing on grapevine virus A (GVA), grapevine fanleaf virus (GFLV), grapevine leafroll-associated virus 3 (GLRaV-3), and grapevine Pinot gris virus (GPGV). This assessment was performed using positive controls and comparing the limit of detection (LoD) results of RT-dPCR against the results obtained by RT-qPCR. ANOVA results showed that the PCR technique (RT-dPCR or RT-qPCR) and the virus (GVA, GLFV, GPGV, and GLRaV-3) were statistically significant in the results of the comparison of LoD. Furthermore, the replicates were non-significant according to ANOVA, showing high repeatability in both RT-qPCR and RT-dPCR. Tukey test demonstrated that RT-dPCR is significantly more sensitive than RT-qPCR, with a statistically reliable difference of 95% trust, especially in low-viral-load viruses such GPGV, which detection showed also to be statistically different than the other viruses. Additionally, a field study was performed to identify the presence or absence of each virus in 45 grapevine samples evaluated with RT-qPCR and RT-dPCR. Several false negative results were generated by RT-qPCR, which only reported positive results to 62.5% of the GVA infected samples, 85.7% of the samples contaminated with GLRaV-3, 38.9% of GPGV positive samples and for GFLV only 12.5% of the infected samples were identified. These results confirm the effectiveness of RT-dPCR as a sensitive method for RNA virus detection in grapevine, enabling early diagnosis and optimal management of viral infections in grapevine crops.
- A comparison between Machine Learning models for OSA detection based on ECG signal(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12-04) Espinosa Varela, Miguel Angel; Ponce Cruz, Pedro; emimmayorquin; Ponce, Pedro; Rojas, Mario; Borja, Vicente; Mata, Omar; Molina, Arturo; School of Engineering and Sciences; Campus Ciudad de México; Rojas Hernández, MarioOSA is a one of the most common sleep disorders nowadays, which is diagnosed by a Polysomnography (PSG) study. Even though this is the golden standard to diagnose OSA, it is time consuming, very expensive and there are not many specialized centers to conduct it, this implies that fewer patients are diagnosed. The development of new solutions at a lower cost and in less time would allow more patients to be diagnosed and treated promptly. There are solutions that enable the diagnosis of OSA through monitoring signals from the human body, including an auto-diagnosis. However, these solutions do not aim to perform screening on the most significant parameters along with the best model for making predictions. The main objective of this tesis is to make a comparison between 27 Machine Learning (ML) models in order to find the best model to diagnose OSA. It also aims to find which are the most representative parameters in OSA detection. By doing a frequency-domain, time-domain and non-linear domain analysis to extract them from the RR intervals, and with a wilcoxon test and correlation matrix, select the most useful ones. The results showed that the best model was Support Vector Machine (SVM) with an accuracy, balanced accuracy, ROC AUC and F1 Score of 0.97. The most significant parameters found were: RR tri index, LF/HF ratio, alpha 2, HF\%, SDNN and RMSSD. This solution can be integrated into current clinical cases for a quick OSA diagnosis. Proposal does not aim to replace PSG for a complete and accurate diagnosis, but to be a pre diagnosis accessible to a larger number of patients. Health providers can implement this solution and reduce the number of patients in the waiting list. Also, this proposal would make research in OSA diagnosis more accessible and provide a framework that can be the starting point to other researchs.
- Wavelets for spindle health diagnosis(2018-12-04) Villagómes Garzón, Silvia Cristina; Morales Menéndez, Rubén; Vallejo Guevara, Antonio; Hernández Alcántara, DianaIndustrial development and customer demands have increased the need to look for high-quality products at low cost and, at the same time, ensure safety during manufacturing. As a result, rotary machinery and its components have become increasingly complex, making their repairs more expensive. Therefore, many efforts must be focused in preventing breakdowns in machines, for which real-time fault diagnosis and prognosis are mandatory. Considering that the element most prone to failure in a machining center is the spindle, and with it its bearing system, the diagnosis of failures of these elements is of paramount importance. To ensure the safe operation of the bearing, some methods of fault detection have been developed based on different techniques. One of the most commonly used is vibration analysis. There are several difficulties when dealing with analyzing vibration signals, they are complex and non-stationary signals with a large amount of noise. Conventional analysis have not been able to solve this problem, thus, alternative methods such as Wavelet Transform have been gaining ground. The following research is focused in detecting bearing faults, as well as the main shaft faults, which eventually also lead to bearing damage, by using wavelets. Different signals, presenting distinct bearing fault conditions, of different data sets are evaluated for validating the proposed methodology. An exhaustive analysis has been developed for selecting the best parameters of this methodology. As results, an improvement around 20% in magnitude of bearing fault frequency peaks was found, compared to the traditional methodology. The proposal of giving more weight to high energy components allows increasing these fault frequencies, as well as reducing low frequency noise. This provides a great advantage in pursuit of an automatic fault detection. An industrial approach was also validated, by proving that the proposed methodology is more immune to noise. Even though, the magnitudes of the bearing fault peaks are diminished by noise, a comparison between the proposal and the traditional methodology reveal an increase of approximately 70% of those magnitudes. Demonstrating that the fault information is barely attenuated by noise. Also, an early diagnosis was proved, which could benefit future studies of fault prognosis. Finally, the filtering property of wavelet decomposition is exploited to limit the frequencies of the signal to few harmonics of the shaft speed. This with the aim of restricting the spectrum for detecting other faults, that mainly affect the spindle shaft, which are diagnosed by analyzing speed harmonics and subharmonics. Thus, a complete methodology is proposed to deal with the main spindle faults.