Trvpodendron lineatum

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Strain Modulated Superlattices in Graphene

Strain engineering of graphene takes advantage of one of the most dramatic responses of Dirac electrons enabling their manipulation via strain-induced pseudo-magnetic fields. Numerous theoretically proposed devices, such as resonant cavities and valley filters, as well as novel phenomena, such as snake states, could potentially be enabled via this effect. These proposals, however, require strong, spatially oscillating magnetic fields while to date only the generation and effects of pseudo-gauge fields which vary at a length scale much larger than the magnetic length have been reported. Here we create a periodic pseudo-gauge field profile using periodic strain that varies at the length scale comparable to the magnetic length and study its effects on Dirac electrons. A periodic strain profile is achieved by pulling on graphene with extreme (>10%) strain and forming nanoscale ripples, akin to a plastic wrap pulled taut at its edges. Combining scanning tunneling microscopy and atomistic calculations, we find that spatially oscillating strain results in a new quantization different from the familiar Landau quantization observed in previous studies. We also find that graphene ripples are characterized by large variations in carbon-carbon bond length, directly impacting the electronic coupling between atoms, which within a single ripple can be as different as in two different materials. The result is a single graphene sheet that effectively acts as an electronic superlattice. Our results thus also establish a novel approach to synthesize an effective 2D lateral heterostructure - by periodic modulation of lattice strain.Comment: 18 pages, 5 figures and supplementary informatio

TNF-α induced endothelial MAdCAM-1 expression is regulated by exogenous, not endogenous nitric oxide

BACKGROUND: MAdCAM-1 is an adhesion molecule expressed in Peyer's patches and lymphoid tissues which is mobilized by cytokines like TNF-α and is a major determinant of lymphocyte trafficking to the gut in human inflammatory bowel disease (IBD). It has been suggested that both reactive oxygen and nitrogen metabolites participate in regulating adhesion molecule expression in response to TNF-α. METHODS: To examine how exogenous and endogenous sources of NO modulate MAdCAM-1 induction by TNF-α, we pre-treated mouse lymphatic endothelial cells with either long or short acting NO donors prior to TNF-α-stimulation, and measured MAdCAM-1 induction at 24 h. RESULTS AND DISCUSSION: DETA-NO, a long-acting NO donor, and SperNO, a rapid releasing NO donor both inhibited TNF-α-stimulated MAdCAM-1 expression in a concentration dependent manner. Both NO donors also reduced a4b7-dependent lymphocyte endothelial adhesion. Inhibition of endogenous NO production by either L-NAME, a non-selective NOS inhibitor, or by 1400 w, a selective iNOS inhibitor failed to induce, or potentiate TNF-α regulated MAdCAM-1 expression. CONCLUSIONS: Exogenous NO donors may be beneficial in the treatment of IBD, while endogenous nitric oxide synthases may be less effective in controlling adhesion molecule expression in response to cytokines

Family History of Alcoholism and Childhood Adversity: Joint Effects on Alcohol Consumption and Dependence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65494/1/j.1530-0277.1994.tb00085.x.pd

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers