Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

Introduction: Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods: Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results: Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion: These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.journal articl

Secondary EML4?ALK-positive Lung Adenocarcinoma in a Patient Previously Treated for Acute Lymphoblastic Leukemia in Childhood: A Case Reportated for Acute Lymphoblastic Leukemia in Childhood: A Case Report

It is widely recognized that the risk of secondary neoplasms increases as childhood cancer survivors progress through adulthood. These are mainly hematological malignancies, and recurrent chromosome translocations are commonly detected in such cases. On the other hand, while secondary epithelial malignancies have sometimes been reported, chromosome translocations in these epithelial malignancies have not. A 33-year-old man who had been diagnosed with acute lymphoblastic leukemia and treated with chemotherapy almost 20 years earlier was diagnosed with lung adenocarcinoma. After chromosomal rearrangement of echinoderm microtubule- associated protein-like 4 gene and the anaplastic lymphoma kinase gene was detected in this adenocarcinoma, he responded to treatment with crizotinib. It was therefore concluded that this echinoderm microtubule-associated protein-like 4 gene-anaplastic lymphoma kinase gene-positive lung adenocarcinoma was a secondary epithelial malignancy

Transforming somatic mutations of mammalian target of rapamycin kinase in human cancer

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase that acts downstream of the phosphatidylinositol 3-kinase signaling pathway and regulates a wide range of cellular functions including transcription, translation, proliferation, apoptosis, and autophagy. Whereas genetic alterations that result in mTOR activation are frequently present in human cancers, whether the mTOR gene itself becomes an oncogene through somatic mutation has remained unclear. We have now identified a somatic non-synonymous mutation of mTOR that results in a leucine-to-valine substitution at amino acid position 2209 in a specimen of large cell neuroendocrine carcinoma. The mTOR(L2209V) mutant manifested marked transforming potential in a focus formation assay with mouse 3T3 fibroblasts, and it induced the phosphorylation of p70 S6 kinase, S6 ribosomal protein, and eukaryotic translation initiation factor 4E-binding protein 1 in these cells. Examination of additional tumor specimens as well as public and in-house databases of cancer genome mutations identified another 28 independent non-synonymous mutations of mTOR in various cancer types, with 12 of these mutations also showing transforming ability. Most of these oncogenic mutations cluster at the interface between the kinase domain and the FAT (FRAP, ATM, TRRAP) domain in the 3-D structure of mTOR. Transforming mTOR mutants were also found to promote 3T3 cell survival, and their oncogenic activity was sensitive to rapamycin. Our data thus show that mTOR acquires transforming activity through genetic changes in cancer, and they suggest that such tumors may be candidates for molecularly targeted therapy with mTOR inhibitors

Characterization of Highly Pathogenic Avian Influenza Virus A(H5N6), Japan, November 2016

Highly pathogenic avian influenza viruses (HPAIVs) A(H5N6) were concurrently introduced into several distant regions of Japan in November 2016. These viruses were classified into the genetic clade 2.3.4.4c and were genetically closely related to H5N6 HPAIVs recently isolated in South Korea and China. In addition, these HPAIVs showed further antigenic drift

KLC1-ALK: A Novel Fusion in Lung Cancer Identified Using a Formalin-Fixed Paraffin-Embedded Tissue Only

The promising results of anaplastic lymphoma kinase (ALK) inhibitors have changed the significance of ALK fusions in several types of cancer. These fusions are no longer mere research targets or diagnostic markers, but they are now directly linked to the therapeutic benefit of patients. However, most available tumor tissues in clinical settings are formalin-fixed and paraffin-embedded (FFPE), and this significantly limits detailed genetic studies in many clinical cases. Although recent technical improvements have allowed the analysis of some known mutations in FFPE tissues, identifying unknown fusion genes by using only FFPE tissues remains difficult. We developed a 5′-rapid amplification of cDNA ends-based system optimized for FFPE tissues and evaluated this system on a lung cancer tissue with ALK rearrangement and without the 2 known ALK fusions EML4-ALK and KIF5B-ALK. With this system, we successfully identified a novel ALK fusion, KLC1-ALK. The result was confirmed by reverse transcription-polymerase chain reaction and fluorescence in situ hybridization. Then, we synthesized the putative full-length cDNA of KLC1-ALK and demonstrated the transforming potential of the fusion kinase with assays using mouse 3T3 cells. To the best of our knowledge, KLC1-ALK is the first novel oncogenic fusion identified using only FFPE tissues. This finding will broaden the potential value of archival FFPE tissues and provide further biological and clinical insights into ALK-positive lung cancer

Comprehensive Computational Simulation Framework for Atypical Development using Hierarchical Bayesian Neural Network Model

Investigating the pathological mechanisms of developmental disorders including autism spectrum disorder is a challenge, because of the fact that the presented symptoms are a result of complex and dynamic factors such as genes, molecules, neural networks, cognitive behavior, environment, and developmental learning. In recent years, computational methods, including the Bayesian brain hypothesis, have been expected to provide a unified framework for understanding developmental disorders that explain the neurocognitive functions by describing the interactions between these factors. However, this approach is still limited because most studies have focused on cross-sectional task performance and lacked the perspectives of developmental learning (i.e., acquisitions of knowledge reflecting probabilistic and hierarchical structures in the environment). In this study, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as ``in silico neurodevelopment framework for atypical representation learning.'' Using the proposed framework, simple simulation experiments were conducted to examine whether manipulating the neural stochasticity and noise levels in external environments can lead to the abnormal acquisition of hierarchical Bayesian representation and reduced flexibility. Consequently, networks with normal neural stochasticity were able to acquire hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good flexibility. When the neural stochasticity was high, top-down generation using higher-order representation was impaired, although the flexibility did not differ from that of the normal settings. On the other hand, the networks with extremely low neural stochasticity demonstrated reduced flexibility and abnormal hierarchical representation. However, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. These results demonstrated that our proposed method is useful for investigating developmental disorders with bridging multiple factors including the inherent characteristics of the neural dynamics, acquisitions of hierarchical representation, and external environment