Treatment of Patients with Advanced Hepatocellular Carcinoma using Stem Cell Differentiation Stage Factors

Hepatocellular carcinoma (HCC) represents the third leading cause of cancer-related death. Because HCC is multicentric with time, excluding the few transplanted patients, sooner or later it becomes untreatable with loco-regional therapies and, until some years ago, it was not responsive to systemic therapies. In 2005 a randomized trial indicated the efficacy of a product containing stem cell differentiation stage factors (SCDSF) taken from zebrafish embryos during the stage in which the totipotent stem cells are differentiating into the pluripotent adult stem cells. In such a trial the patients, with “intermediate” and “advanced” HCC according to Barcelona Clinic Liver Cancer/American Association for the Study of Liver Diseases (BCLC/AASLD) guidelines, presented benefit in terms of performance status and objective tumoral response, with some cases (2.4%) of complete response, that is, sustained disappearance of the neoplastic areas or blood supply therein, accompanied by normalization of alpha-fetoprotein (AFP) levels (complete response [CR]). A second study reporting the experience of a tertiary referral center on patients with “advanced” stage confirmed the possibility to obtain CR. Out of 38 patients, 5 presented a sustained CR (13.1%). Improvement on PS was obtained in 17 patients (34.6%). No side effects occurred.     

Neural synchronization as a hypothetical explanation of the psychoanalytic unconscious

Cognitive scientists have tried to explain the neural mechanisms of unconscious mental states such as coma, epileptic seizures, and anesthesia-induced unconsciousness. However these types of unconscious states are different from the psychoanalytic unconscious. In this review, we aim to present our hypothesis about the neural correlates underlying psychoanalytic unconscious. To fulfill this aim, we firstly review the previous explanations about the neural correlates of conscious and unconscious mental states, such as brain oscillations, synchronicity of neural networks, and cognitive binding. By doing so, we hope to lay a neuroscientific ground for our hypothesis about neural correlates of psychoanalytic unconscious; parallel but unsynchronized neural networks between different layers of consciousness and unconsciousness. Next, we propose a neuroscientific mechanism about how the repressed mental events reach the conscious awareness; the lock of neural synchronization between two mental layers of conscious and unconscious. At the last section, we will discuss the data about schizophrenia as a clinical example of our proposed hypothesis.     

Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one-to-many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human-annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view.     

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.     

Multiple Factors and Multiple Mechanisms Determine the Quality of Conscious Experiences: A Reply to Anzulewicz and Wierzchoń

In this Letter to the Editor, we seize the opportunity to respond to the recent comments by Anzulewicz and Wierzchoń, and further clarify and extend the scope of our original paper. We re‐emphasize that conscious experiences come in degrees, and that there are several factors that determine this degree. Endorsing the suggestions of Anzulewicz and Wierzchoń, we discuss that besides low‐level attentional mechanisms, high‐level attentional and non‐attentional mechanisms might also modulate the quality of conscious experiences.     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.     

A key role for experimental task performance: effects of math talent, gender and performance on the neural correlates of mental rotation

The neurophysiological mechanisms underlying superior cognitive performance are a research area of high interest. The majority of studies on the brain-performance relationship assessed the effects of capability-related group factors (e.g. talent, gender) on task-related brain activations while only few studies examined the effect of the inherent experimental task performance factor. In this functional MRI study, we combined both approaches and simultaneously assessed the effects of three relatively independent factors on the neurofunctional correlates of mental rotation in same-aged adolescents: math talent (gifted/controls: 17/17), gender (male/female: 16/18) and experimental task performance (median split on accuracy; high/low: 17/17). Better experimental task performance of mathematically gifted vs. control subjects and male vs. female subjects validated the selected paradigm. Activation of the inferior parietal lobule (IPL) was identified as a common effect of mathematical giftedness, gender and experimental task performance. However, multiple linear regression analyses (stepwise) indicated experimental task performance as the only predictor of parietal activations. In conclusion, increased activation of the IPL represents a positive neural correlate of mental rotation performance, irrespective of but consistent with the obtained neurocognitive and behavioral effects of math talent and gender. As experimental performance may strongly affect task-related activations this factor needs to be considered in capability-related group comparison studies on the brain-performance relationship.     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.