Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.
Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies–Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.
Esta investigación analizó respuestas morfogénicas de semillas de cruzamientos controlados en micropropagación de Eucalyptus nitens. Se utilizó semillas de E. nitens de 6 cruzamientos controlados, para el control se utilizó mezcla familiar. Semillas estériles fueron sembradas en medio MS (1962) modificado, 100 mg/L inositol, 2% sacarosa, 0,7% agar, pH 5,7. Para la inducción de brotes múltiples y elongación utilizó medio MS (1962) modificado, 0.25 mg/L BAP y 0,01 mg/L ANA. El enraizamiento se realizó en medio MS (1962) modificado, con regulador hormonal. Las vitroplántulas fueron mantenidas a 23ºC, fotoperiodo 16/8 h, 25 ?molm-2seg-1. Para 203 clones establecidos de E. nitens, un 69% de ellos desarrollaron brotes múltiples, en 4 ciclos de subcultivo. En las semillas control se diferenciaron 32 clones, donde, un 86,5% desarrolló brotes múltiples. En el cruzamiento A se diferenciaron 26 clones con 100% de multiplicación, para el cruzamiento F se multiplicaron 36% de los clones. De 89 clones diferenciados en semillas de cruzamientos, la cruza B obtuvo un 60% de enraizamiento y un 51% en la cruza E, para 32 clones control solo el 29% enraizaron. Los resultados mostraron que no existieron diferencias significativas en multiplicación, entre las semillas mejoradas y control. Existieron diferencias significativas en enraizamiento entre las cruzas B y E en relación al control. El análisis de resultados se efectuó con ANOVA de una vía con 95% de confianza , Test de Tukey.05 y Statistica versión 6.0. Para graficar se utilizó GraphPad Prism 5
Resumen. En este texto se abordan los aportes específicos del psicoanálisis en la reflexión sobre la temática de la transmisión hacia las nuevas generaciones -los nietos -de los eventos traumáticos que no han podido ser elaborados por las víctimas familiares antepasadas -los abuelos -en el contexto de la Violencia de Estado ejercida en Chile entre los años 1973 y 1989. Tras la contextualización de algunos hitos significativos de la dictadura chilena, se conceptualizarán las nociones de trauma, historia y transmisión de lo traumático desde una perspectiva psicoanalítica para, finalmente, reflexionar sobre los casos de Cecilia y Margarita, dos jóvenes cuyas familias fueron desintegradas por la Violencia de Estado. La transmisión muestra una historia que debe reconstruirse por medio de los detalles relatados en la biografía testimoniada, centrándose en aquello que se repite, que interrumpe y que se historiza en la transferencia, cuestionando así aquello invisible del terrorismo de Estado que ha sido transmitido a una generación de personas nacidas después de haber concluido dicho período histórico. Palabras clave: Violencia de Estado; historia; trauma; transmisión de lo traumático. [en] State Violence and Transmission between GenerationsAbstract. This text deals with the specific contributions provided by psychoanalysis to the reflection on the transmission to the new generations -grandchildren-of the trauma which may have been produced by their elders, within their own family -grandparents-in the context of the state violence carried on between 1973 and 1989 in Chile. We will first contextualize some of the most significant milestones of the Chilean dictatorship, then we will conceptualise the notions of trauma, history and trauma transmission from a psychoanalytic perspective, so as to reflect on the cases of Cecilia and Margarita, two youngsters whose families were disintegrated by state violence. Transmission reveals the need of history to reconstruct itself through the details related in testimonial biography, focused on what repeats, interrupts and is recorded through transfer; thus it questions what makes transmission invisible in state terrorism, passed on to a generation of people born after this historical period took end.
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