Displacement field and elastic constants in non-ideal crystals

In this work a periodic crystal with point defects is described in the framework of linear response theory for broken symmetry states using correlation functions and Zwanzig-Mori equations. The main results are microscopic expressions for the elastic constants and for the coarse-grained density, point-defect density, and displacement field, which are valid in real crystals, where vacancies and interstitials are present. The coarse-grained density field differs from the small wave vector limit of the microscopic density. In the long wavelength limit, we recover the phenomenological description of elasticity theory including the defect density.Comment: Phys Rev. B, in print (2010

Competition of crystal field splitting and Hund's rule coupling in two-orbital magnetic metal-insulator transitions

Competition of crystal field splitting and Hund's rule coupling in magnetic metal-insulator transitions of half-filled two-orbital Hubbard model is investigated by multi-orbital slave-boson mean field theory. We show that with the increase of Coulomb correlation, the system firstly transits from a paramagnetic (PM) metal to a {\it N\'{e}el} antiferromagnetic (AFM) Mott insulator, or a nonmagnetic orbital insulator, depending on the competition of crystal field splitting and the Hund's rule coupling. The different AFM Mott insulator, PM metal and orbital insulating phase are none, partially and fully orbital polarized, respectively. For a small $J_{H}$ and a finite crystal field, the orbital insulator is robust. Although the system is nonmagnetic, the phase boundary of the orbital insulator transition obviously shifts to the small $U$ regime after the magnetic correlations is taken into account. These results demonstrate that large crystal field splitting favors the formation of the orbital insulating phase, while large Hund's rule coupling tends to destroy it, driving the low-spin to high-spin transition.Comment: 4 pages, 4 figure

State Transition Algorithm

In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.Comment: 18 pages, 28 figure

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Martin Heidegger’s Concept of Understanding (Verstehen): An Inquiry into Artificial Intelligence

My primary goal in this paper is to demonstrate the inadequacy of Hubert Dreyfus’ use of understanding (Verstehen) for Artificial Intelligence (AI). My complementary goal is to provide a principled account of Martin Heidegger’s concept of understanding (Verstehen). Dreyfus and other verificationists argue that understanding (Verstehen) is socially purposive action and skillful embodied coping. Understanding (Verstehen), conceived of in this way, purportedly challenges cognitive models of Artificial Intelligence (AI) that rely on formal rules, ‘rational’ decisionmaking, and the explicit representation of knowledge. This account is unsatisfactory for two reasons. First, it maintains an extrinsic, goal-oriented intentionality that is susceptible to the success of Artificial Intelligence (AI). Second, it ignores the systematic and constitutive analysis of self-understanding (Seinsverständnis) that is fundamental to Heidegger’s ontology. Recent exegetical work replicates these inadequacies and fails to improve discussions on Heidegger’s relationship to Artificial Intelligence (AI). To resolve this oversight, I bridge the gap between Heidegger’s concept of understanding and disclosedness (Erschlossenheit) (SZ §44 / 256-278). I argue that understanding characterizes the pre-theoretical grasp of entities and the pre-ontological structure that initiates the question of self-understanding (Seinsverständnis)

A Principled Account of Artistic Sublimity in Kant’s Critique of Judgment

A curious feature in Immanuel Kant’s account of the mathematical sublime is the choice of examples, namely, the Pyramids of Egypt and St. Peter’s Basilica. In the paragraph following these examples, Kant suggests that the sublime does not exhibit itself in works of art. This ambiguity has led scholars to question the possibility of “artistic sublimity.” The scholarship has prompted discussions about whether works of art that evoke the sublime feeling are genuine sublime experiences. A representational account of artistic sublimity restricts the sublime to experiences in raw nature. Art can depict the sublime stylistically; however, it cannot evoke the feeling of sublimity. On an opposing interpretation, the sublime occurs because of a conflict between imagination and reason; any work of art that provokes this conflict is sublime. As a result, the power of the sublime overrides and annihilates determinate ends, allowing pure aesthetic judgments. The alleged contradiction in Kant’s account of the mathematical sublime is also read as an interpretive issue regarding purity, prompting the pure-impure distinction. Art that has the power to evoke the feeling of sublimity is deemed “impurely sublime” because of the admixture of interest. Alternatively, if aesthetic judgments of the sublime are satisfied irrespective of the object, then art is purely sublime without needing a further determination. Following Henry Allison, I defend the view that we should preserve a pure–impure distinction. A concept of impure sublimity allows us to account for an emotionally motiving satisfaction akin to the sublime in the presence of some works of art while maintaining a close reading of the Kantian account of pure sublimity as an exclusive consequence of raw nature