MIMo: A Multi-Modal Infant Model for Studying Cognitive Development

Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds with similar properties. Importantly, human cognitive development relies on embodied interactions with the physical and social environment, which is perceived via complementary sensory modalities. These interactions allow the developing mind to probe the causal structure of the world. This is in stark contrast to common machine learning approaches, e.g., for large language models, which are merely passively ``digesting'' large amounts of training data, but are not in control of their sensory inputs. However, computational modeling of the kind of self-determined embodied interactions that lead to human intelligence and consciousness is a formidable challenge. Here we present MIMo, an open-source multi-modal infant model for studying early cognitive development through computer simulations. MIMo's body is modeled after an 18-month-old child with detailed five-fingered hands. MIMo perceives its surroundings via binocular vision, a vestibular system, proprioception, and touch perception through a full-body virtual skin, while two different actuation models allow control of his body. We describe the design and interfaces of MIMo and provide examples illustrating its use. All code is available at https://github.com/trieschlab/MIMo .Comment: 11 pages, 8 figures. Submitted to IEEE Transactions on Congnitive and Developmental Systems (TCDS

Lactate-Dehydrogenase 5 is overexpressed in non-small cell lung cancer and correlates with the expression of the transketolase-like protein 1

<p>Abstract</p> <p>Aims</p> <p>As one of the five Lactate dehydrogenase (LDH) isoenzymes, LDH5 has the highest efficiency to catalyze pyruvate transformation to lactate. LDH5 overexpression in cancer cells induces an upregulated glycolytic metabolism and reduced dependence on the presence of oxygen. Here we analyzed LDH5 protein expression in a well characterized large cohort of primary lung cancers in correlation to clinico-pathological data and its possible impact on patient survival.</p> <p>Methods</p> <p>Primary lung cancers (n = 269) and non neoplastic lung tissue (n = 35) were tested for LDH5 expression by immunohistochemistry using a polyclonal LDH5 antibody (ab53010). The results of LDH5 expression were correlated to clinico-pathological data as well as to patient's survival. In addition, the results of the previously tested Transketolase like 1 protein (TKTL1) expression were correlated to LDH5 expression.</p> <p>Results</p> <p>89.5% (n = 238) of NSCLC revealed LDH5 expression whereas LDH5 expression was not detected in non neoplastic lung tissues (n = 34) (p < 0.0001). LDH5 overexpression was associated with histological type (adenocarcinoma = 57%, squamous cell carcinoma = 45%, large cell carcinoma = 46%, p = 0.006). No significant correlation could be detected with regard to TNM-stage, grading or survival. A two sided correlation between the expression of TKTL1 and LDH5 could be shown (p = 0.002) within the overall cohort as well as for each grading and pN group. A significant correlation between LDH5 and TKTL1 within each histologic tumortype could not be revealed.</p> <p>Conclusions</p> <p>LDH5 is overexpressed in NSCLC and could hence serve as an additional marker for malignancy. Furthermore, LDH5 correlates positively with the prognostic marker TKTL1. Our results confirm a close link between the two metabolic enzymes and indicate an alteration in the glucose metabolism in the process of malignant transformation.</p

Entwickung und Evaluation eines Fahrzeugsimulators für künstliche DNA

Es sollte eine Simulationsumgebung mit einem Straßennetz und eine KDNA, die Autos auf diesem Straßennetz kontrolliert, implementiert werden. Für die Simulation wurde eine einfache graphische Darstellung entwickelt auf der eine variable Anzahl Autos auf einem vorprogrammierten Straßennetz fahren. Eine KDNA steuert diese Autos über Kontrolle von Gas-, Bremse- und Steuerradpositionen, wobei Geschwindigkeits- und Richtungskontrolle unabhängig stattfinden. Bei der Analyse der KDNA für mehrere Autos traten Leistungsprobleme auf, deren Quelle genauer untersucht wurde. Die Last wurde primär durch die Kommunikation zur Verwaltung der KDNA-Tasks im AHS erzeugt

Quantitative Phylogenetic Analysis in the 21st Century Análisis Filogenéticos Cuantitativos en el siglo XXI

We review Hennigian phylogenetics and compare it with Maximum parsimony, Maximum likelihood, and Bayesian likelihood approaches. All methods use the principle of parsimony in some form. Hennigian-based approaches are justified ontologically by the Darwinian concepts of phylogenetic conservatism and cohesion of homologies, embodied in Hennig's Auxiliary Principle, and applied by outgroup comparisons. Parsimony is used as an epistemological tool, applied a posteriori to choose the most robust hypothesis when there are conflicting data. Quantitative methods use parsimony as an ontological criterion: Maximum parsimony analysis uses unweighted parsimony, Maximum likelihood weight all characters equally that explain the data, and Bayesian likelihood relying on weighting each character partition that explains the data. Different results most often stem from insufficient data, in which case each quantitative method treats ambiguities differently. All quantitative methods produce networks. The networks can be converted into trees by rooting them. If the rooting is done in accordance with Hennig's Auxiliary Principle, using outgroup comparisons, the resulting tree can then be interpreted as a phylogenetic hypothesis. As the size of the data set increases, likelihood methods select models that allow an increasingly greater number of a priori possibilities, converging on the Hennigian perspective that nothing is prohibited a priori. Thus, all methods produce similar results, regardless of data type, especially when their networks are rooted using outgroups. Appeals to Popperian philosophy cannot justify any kind of phylogenetic analysis, because they argue from effect to cause rather than from cause to effect. Nor can particular methods be justified on the basis of statistical consistency, because all may be consistent or inconsistent depending on the data. If analyses using different types of data and/or different methods of phylogeny reconstruction do not produce the same results, more data are needed.<br>Se revisa la sistemática filogenética Hennigiana y se compara con las aproximaciones de Máxima Parsimonia, Máxima Verosimilitud y verosimilitud Bayesiana. Todos los métodos utilizan el principio de la parsimonia en alguna forma. Las aproximaciones con bases Hennigianas se justifican ontológicamente con los conceptos Darwinianos de conservacionismo filogenético y cohesión de las homologías, representados en el Principio Auxiliar de Hennig, y aplicado en la comparación con el grupo externo. La Parsimonia se utiliza como una herramienta epistemológica, aplicada a posteriori en la elección de la hipótesis más robusta cuando hay datos en conflicto. Los métodos cuantitativos utilizan la parsimonia como un criterio ontológico: los análisis de Máxima Parismonia utilizan la parsimonia sin pesaje, la Máxima Verosimilitud les asigna un peso igual a todos los caracteres que explican los datos, mientras que la verosimilitud Bayesiana depende del pesaje de cada una de las particiones de caracteres que explican los datos. Las diferencias en los resultados derivan de un muestreo insuficiente de datos, en cuyo caso cada método trata las ambigüedades de manera diferente. Todos los métodos cuantitativos producen redes. Las redes pueden convertirse en árboles al ser enraizadas. Si el enraizamiento se efectua de acuerdo con el Principio Auxiliar de Hennig, utilizando la comparación con un grupo externo, el árbol resultante puede considerarse como una hipótesis filogenética. Al incrementarse el número de datos, los métodos de verosimilitud selccionan modelos que permiten un número cada vez mayor de posibilidades a priori, convergiendo en la perspectiva Hennigiana de que nada está prohibido a priori. Por lo tanto, todos los métodos producen resultados similares independientemente del tipo de datos, especialmente cuando las redes se enraizan utilizando grupos externos. Las invocaciones a la filosofia Popperiana no pueden justificar ningún tipo de análisis filogenético, ya que sus argumentos van del efecto a la causa y no de la causa al efecto. Tampoco se puede justificar el uso de un método en particular con base en la consistencia estadística, ya que todos pueden ser consistentes o incosistentes dependiendo de los datos. Si los análisis con diferentes tipos de datos y/o métodos de reconstrucción filogenética no producen igual resultado, significa que es necesario reunir datos adicionales