Scalable Empirical Dynamic Modeling With Parallel Computing and Approximate k-NN Search

Empirical Dynamic Modeling (EDM) is a mathematical framework for modeling and predicting non-linear time series data. Although EDM is increasingly adopted in various research fields, its application to large-scale data has been limited due to its high computational cost. This article presents kEDM, a high-performance implementation of EDM for analyzing large-scale time series datasets. kEDM adopts the Kokkos performance-portable programming model to efficiently run on both CPU and GPU while sharing a single code base. We also conduct hardware-specific optimization of performance-critical kernels. kEDM achieved up to 6.58× speedup in pairwise causal inference of real-world biology datasets compared to an existing EDM implementation. Furthermore, we integrate multiple approximate k-NN search algorithms into EDM to enable the analysis of extremely large datasets that were intractable with conventional EDM based on exhaustive k-NN search. EDM-based time series forecast enhanced with approximate k-NN search demonstrated up to 790× speedup compared to conventional Simplex projection with less than 1% increase in MAPE.journal articl

An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors.

To address the biological heterogeneity of lung cancer, we studied 199 lung adenocarcinomas by integrating genome-wide data on copy number alterations and gene expression with full annotation for major known somatic mutations in this cancer. This showed non-random patterns of copy number alterations significantly linked to EGFR and KRAS mutation status and to distinct clinical outcomes, and led to the discovery of a striking association of EGFR mutations with underexpression of DUSP4, a gene within a broad region of frequent single-copy loss on 8p. DUSP4 is involved in negative feedback control of EGFR signaling, and we provide functional validation for its role as a growth suppressor in EGFR-mutant lung adenocarcinoma. DUSP4 loss also associates with p16/CDKN2A deletion and defines a distinct clinical subset of lung cancer patients. Another novel observation is that of a reciprocal relationship between EGFR and LKB1 mutations. These results highlight the power of integrated genomics to identify candidate driver genes within recurrent broad regions of copy number alteration and to delineate distinct oncogenetic pathways in genetically complex common epithelial cancers

Genic regions of a large salamander genome contain long introns and novel genes

BACKGROUND: The basis of genome size variation remains an outstanding question because DNA sequence data are lacking for organisms with large genomes. Sixteen BAC clones from the Mexican axolotl (Ambystoma mexicanum: c-value = 32 x 10(9) bp) were isolated and sequenced to characterize the structure of genic regions. RESULTS: Annotation of genes within BACs showed that axolotl introns are on average 10x longer than orthologous vertebrate introns and they are predicted to contain more functional elements, including miRNAs and snoRNAs. Loci were discovered within BACs for two novel EST transcripts that are differentially expressed during spinal cord regeneration and skin metamorphosis. Unexpectedly, a third novel gene was also discovered while manually annotating BACs. Analysis of human-axolotl protein-coding sequences suggests there are 2% more lineage specific genes in the axolotl genome than the human genome, but the great majority (86%) of genes between axolotl and human are predicted to be 1:1 orthologs. Considering that axolotl genes are on average 5x larger than human genes, the genic component of the salamander genome is estimated to be incredibly large, approximately 2.8 gigabases! CONCLUSION: This study shows that a large salamander genome has a correspondingly large genic component, primarily because genes have incredibly long introns. These intronic sequences may harbor novel coding and non-coding sequences that regulate biological processes that are unique to salamanders

A central alarm system that gates multi-sensory innate threat cues to the amygdala

Perception of threats is essential for survival. Previous findings suggest that parallel pathways independently relay innate threat signals from different sensory modalities to multiple brain areas, such as the midbrain and hypothalamus, for immediate avoidance. Yet little is known about whether and how multi-sensory innate threat cues are integrated and conveyed from each sensory modality to the amygdala, a critical brain area for threat perception and learning. Here, we report that neurons expressing calcitonin gene-related peptide (CGRP) in the parvocellular subparafascicular nucleus in the thalamus and external lateral parabrachial nucleus in the brainstem respond to multi-sensory threat cues from various sensory modalities and relay negative valence to the lateral and central amygdala, respectively. Both CGRP populations and their amygdala projections are required for multi-sensory threat perception and aversive memory formation. The identification of unified innate threat pathways may provide insights into developing therapeutic candidates for innate fear-related disorders

Circularity in fisheries data weakens real world prediction

The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.https://www.nature.com/articles/s41598-020-63773-3Published versio

Glioblastoma Model Using Human Cerebral Organoids

Summary: We have developed a cancer model of gliomas in human cerebral organoids that allows direct observation of tumor initiation as well as continuous microscopic observations. We used CRISPR/Cas9 technology to target an HRasG12V-IRES-tdTomato construct by homologous recombination into the TP53 locus. Results show that transformed cells rapidly become invasive and destroy surrounding organoid structures, overwhelming the entire organoid. Tumor cells in the organoids can be orthotopically xenografted into immunodeficient NOD/SCID IL2RG−/− animals, exhibiting an invasive phenotype. Organoid-generated putative tumor cells show gene expression profiles consistent with mesenchymal subtype human glioblastoma. We further demonstrate that human-organoid-derived tumor cell lines or primary human-patient-derived glioblastoma cell lines can be transplanted into human cerebral organoids to establish invasive tumor-like structures. Our results show potential for the use of organoids as a platform to test human cancer phenotypes that recapitulate key aspects of malignancy. : Ogawa et al. show that human cerebral organoids can be used as a platform for tumor formation. CRISPR/Cas9 manipulation of oncogenes/tumor suppressors initiates tumorigenesis in cerebral organoids, allowing microscopic observation of tumor development. Additionally, human cerebral organoids can be used as a platform for tumor cell transplantation. Keywords: cerebral organoid, glioblastoma, glioma, CRISPR/Cas