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Background: Alteration of chemosensitivity or tumor aggressiveness in response to chemotherapy has been reported, and liquid biopsy assessment during chemotherapy for colorectal cancers has confirmed the acquisition of mutations in various oncogenes. However, the occurrence of histological transformation seems to be extremely rare in colorectal cancers, and the few existing case reports of this transformation are from lung cancer and breast cancer. In this report, we describe the histological transformation of clinically aggressive scirrhous-type poorly differentiated adenocarcinoma of the ascending colon to signet-ring cell carcinoma in almost all recurrent tumors that were confirmed by autopsy after response to chemotherapy plus cetuximab. Case presentation: A 59-year-old woman visited our hospital with whole abdominal pain and body weight loss and was diagnosed with scirrhous-type poorly differentiated adenocarcinoma of the ascending colon with aggressive lymph node metastases. The intrinsic chemosensitivity of the tumors was evident upon initiation of mFOLFOX6 plus cetuximab therapy, and right hemicolectomy was performed, and the tumor obviously remained in the peripancreatic area, paraaortic region, or other retroperitoneal areas. The ascending colon tumors mainly consisted of poorly differentiated adenocarcinoma and were not associated with signet-ring cell components except for minute clusters in a few lymphatic emboli in the main tumor. Chemotherapy was continued, and metastases were eliminated at 8 months after the operation; this response was maintained for an additional 4 months. Discontinuation of chemotherapy plus cetuximab resulted in immediate tumor recurrence and rapid expansion, and the patient died of the recurrent tumor 1 year and 2 months after the operation. Autopsy specimens revealed that almost all of the recurrent tumors exhibited transformation and consisted of signet-ring cell histology. Conclusion: This case might suggest that various oncogene mutations or epigenetic changes resulting from chemotherapy, especially regimens that include cetuximab, contribute to the transformation of non-signet-ring cell colorectal carcinoma to signet-ring cell carcinoma histology and can promote the aggressive clinical progression characteristic of signet-ring cell carcinoma. [ABSTRACT FROM AUTHOR]

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community. [ABSTRACT FROM AUTHOR]

The Burkholderia pseudomallei KHW quorum-sensing systems produced N-octanoyl-homoserine lactone, N-decanoyl-homoserine lactone, N-(3-hydroxy)-octanoyl-homoserine lactone, N-(3-hydroxy)-decanoyl-homoserine lactone, N-(3-oxo)-decanoyl-homoserine lactone, and N-(3-oxo)-tetradecanoyl-homoserine lactone. The extracellular secretion of these acyl-homoserine lactones is dependent absolutely on the function of the B. pseudomallei BpeAB-OprB efflux pump.

• Supercomputers enable us to build and simulate human-scale realistic spiking neural networks. • To harness the computational power, modern high-performance technology must be introduced into neuroscience research. • Various large-scale simulation studies including a human-scale cerebellum simulation are reviewed. • Future directions of large-scale simulation studies are discussed. Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, and is expected to increase further. This unprecedented computational power enables us to build and simulate large-scale neural network models composed of tens of billions of neurons and tens of trillions of synapses with detailed anatomical connections and realistic physiological parameters. Such "human-scale" brain simulation could be considered a milestone in computational neuroscience and even in general neuroscience. Towards this milestone, it is mandatory to introduce modern high-performance computing technology into neuroscience research. In this article, we provide an introductory landscape about large-scale brain simulation on supercomputers from the viewpoints of computational neuroscience and modern high-performance computing technology for specialists in experimental as well as computational neurosciences. This introduction to modeling and simulation methods is followed by a review of various representative large-scale simulation studies conducted to date. Then, we direct our attention to the cerebellum, with a review of more simulation studies specific to that region. Furthermore, we present recent simulation results of a human-scale cerebellar network model composed of 86 billion neurons on the Japanese flagship supercomputer K (now retired). Finally, we discuss the necessity and importance of human-scale brain simulation, and suggest future directions of such large-scale brain simulation research. [ABSTRACT FROM AUTHOR]

Abstract Background Changes in DNA methylation in the mammalian genome during development are frequent events and play major roles regulating gene expression and other developmental processes. It is necessary to identify these events so that we may understand how these changes affect normal development and how aberrant changes may impact disease. Results In this study Methylated DNA ImmunoPrecipitation (MeDIP) was used in conjunction with a NimbleGen promoter plus CpG island (CpGi) array to identify Tissue and Developmental Stage specific Differentially Methylated DNA Regions (T-DMRs and DS-DMRs) on a genome-wide basis. Four tissues (brain, heart, liver, and testis) from C57BL/6J mice were analyzed at three developmental stages (15 day embryo, E15; new born, NB; 12 week adult, AD). Almost 5,000 adult T-DMRs and 10,000 DS-DMRs were identified. Surprisingly, almost all DS-DMRs were tissue specific (i.e. methylated in at least one tissue and unmethylated in one or more tissues). In addition our results indicate that many DS-DMRs are methylated at early development stages (E15 and NB) but are unmethylated in adult. There is a very strong bias for testis specific methylation in non-CpGi promoter regions (94%). Although the majority of T-DMRs and DS-DMRs tended to be in non-CpGi promoter regions, a relatively large number were also located in CpGi in promoter, intragenic and intergenic regions (>15% of the 15,979 CpGi on the array). Conclusions Our data suggests the vast majority of unique sequence DNA methylation has tissue specificity, that demethylation has a prominent role in tissue differentiation, and that DNA methylation has regulatory roles in alternative promoter selection and in non-promoter regions. Overall, our studies indicate changes in DNA methylation during development are a dynamic, widespread, and tissue-specific process involving both DNA methylation and demethylation.

Abstract Background A variety of skin cancer susceptibility among mouse strains has allowed identification of genes responsible for skin cancer development. Fifteen Skts loci for skin tumour susceptibility have been mapped so far by using the two-stage skin carcinogenesis model [induced by 7.12-dimethylbenz(a)anthracene (DMBA)/12-O-tetradecanoylphorbol-13-acetate (TPA)]. A few responsible genes have been identified using wild-derived dominant resistant Mus spretus mice, and one has been confirmed as a low penetrance cancer susceptibility gene in a variety of human cancers. Results In the present study, we found that wild-derived PWK mice developed no tumour by treatment with the two-stage skin carcinogenesis protocol. This phenotype is dominant resistant when crossed with the highly susceptible strain FVB. By analyzing the F1 backcross generation between PWK and FVB, we found empirical evidence of significant linkage at the new loci Skts-fp1 on chromosome 4 and suggestive linkage on chromosomes 1, 3, 11, 12 and 14 for skin tumour susceptibility. Skts-fp1 includes the Skts7 interval, which was previously mapped by a Mus spretus and NIH backcross. We also observed suggestive linkage on chromosomes 1 and 2 in the female population only, while suggestive linkage on chromosomes 14 and 15 only was observed in the male population. A significant genetic interaction was seen between markers of D11Mit339 and D16Mit14. Conclusion Analysis of this new cross may facilitate the identification of genes responsible for mouse skin cancer susceptibility and may reveal their biological interactions.

Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator. [ABSTRACT FROM AUTHOR]

One of the grand challenges for computational neuroscience and high-performance computing is computer simulation of a human-scale whole brain model with spiking neurons and synaptic plasticity using supercomputers. To achieve such a simulation, the target network model must be partitioned onto a number of computational nodes, and the sub-network models are executed in parallel while communicating spike information across different nodes. However, it remains unclear how the target network model should be partitioned for efficient computing on next generation of supercomputers. Specifically, reducing the communication of spike information across compute nodes is essential, because of the relatively slower network performance than processor and memory. From the viewpoint of biological features, the cerebral cortex and cerebellum contain 99% of neurons and synapses and form layered sheet structures. Therefore, an efficient method to split the network should exploit the layered sheet structures. In this study, we indicate that a tile partitioning method leads to efficient communication. To demonstrate it, a simulation software called MONET (Millefeuille-like Organization NEural neTwork simulator) that partitions a network model as described above was developed. The MONET simulator was implemented on the Japanese flagship supercomputer K, which is composed of 82,944 computational nodes. We examined a performance of calculation, communication and memory consumption in the tile partitioning method for a cortical model with realistic anatomical and physiological parameters. The result showed that the tile partitioning method drastically reduced communication data amount by replacing network communication with DRAM access and sharing the communication data with neighboring neurons. We confirmed the scalability and efficiency of the tile partitioning method on up to 63,504 compute nodes of the K computer for the cortical model. In the companion paper by Yamaura et al., the performance for a cerebellar model was examined. These results suggest that the tile partitioning method will have advantage for a human-scale whole-brain simulation on exascale computers. [ABSTRACT FROM AUTHOR]

The cerebellum is a part of the brain that plays essential roles in real-time motor learning and control and even cognitive functions. Thanks to the large amount of anatomical and physiological data, we implemented a spiking network model of the cerebellum on Shoubu, an energy-efficient supercomputer with 1280 PEZY-SC processors at RIKEN. Our artificial cerebellum consists of more than one billion neurons, which is comparable to the whole cerebellum of a cat. Using 1008 of 1280 processors on Shoubu, we achieved real-time simulation, that is, a computer simulation of the model for 1 s completes within 1 s of the real world with temporal resolution of 1 ms. Effective performance was estimated as 68 teraflops in single-precision floating points, suggesting 2.6% of peak performance. We expect that the artificial cerebellum will be applicable to various engineering applications, such as robotics and brain-style artificial intelligence. [ABSTRACT FROM AUTHOR]

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10% of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems. [ABSTRACT FROM AUTHOR]