Deep neural network for x-ray photoelectron spectroscopy data analysis

In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental x-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large (<100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon. The trained net performs as well as standard methods on a test set of 48500 well characterized experimental x-ray photoelectron spectra. Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed. Given the synthetic nature of the training set, this approach could be applied to the automatization of any photoelectron spectroscopy system, without the need of experimental reference spectra and with a low computational effort

Sheldon-Hall syndrome

Sheldon-Hall syndrome (SHS) is a rare multiple congenital contracture syndrome characterized by contractures of the distal joints of the limbs, triangular face, downslanting palpebral fissures, small mouth, and high arched palate. Epidemiological data for the prevalence of SHS are not available, but less than 100 cases have been reported in the literature. Other common clinical features of SHS include prominent nasolabial folds, high arched palate, attached earlobes, mild cervical webbing, short stature, severe camptodactyly, ulnar deviation, and vertical talus and/or talipes equinovarus. Typically, the contractures are most severe at birth and non-progressive. SHS is inherited in an autosomal dominant pattern but about half the cases are sporadic. Mutations in either MYH3, TNNI2, or TNNT3 have been found in about 50% of cases. These genes encode proteins of the contractile apparatus of fast twitch skeletal muscle fibers. The diagnosis of SHS is based on clinical criteria. Mutation analysis is useful to distinguish SHS from arthrogryposis syndromes with similar features (e.g. distal arthrogryposis 1 and Freeman-Sheldon syndrome). Prenatal diagnosis by ultrasonography is feasible at 18–24 weeks of gestation. If the family history is positive and the mutation is known in the family, prenatal molecular genetic diagnosis is possible. There is no specific therapy for SHS. However, patients benefit from early intervention with occupational and physical therapy, serial casting, and/or surgery. Life expectancy and cognitive abilities are normal

Spectroscopic evidence of in-gap states at the SrTiO3/LaAlO3 ultrathin interfaces

Experimental evidence of differences in the electronic properties of an insulating and a conducting SrTiO3/LaAlO3 interface is provided by soft x-ray spectroscopies. While core level photoemission measurements show that only at the conducting interface Ti ions with 3+ ionization state are present, by using resonant photoemission and x-ray absorption spectroscopies, it is shown that in both samples in-gap states with a Ti 3d character are present, but their density is higher at the conducting interface

SLC2A10 genetic polymorphism predicts development of peripheral arterial disease in patients with type 2 diabetes. SLC2A10 and PAD in type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Recent data indicate that loss-of-function mutation in the gene encoding the facilitative glucose transporter GLUT10 (<it>SLC2A10</it>) causes arterial tortuosity syndrome via upregulation of the TGF-β pathway in the arterial wall, a mechanism possibly causing vascular changes in diabetes.</p> <p>Methods</p> <p>We genotyped 10 single nucleotide polymorphisms and one microsatellite spanning 34 kb across the <it>SLC2A10 </it>gene in a prospective cohort of 372 diabetic patients. Their association with the development of peripheral arterial disease (PAD) in type 2 diabetic patients was analyzed.</p> <p>Results</p> <p>At baseline, several common SNPs of <it>SLC2A10 </it>gene were associated with PAD in type 2 diabetic patients. A common haplotype was associated with higher risk of PAD in type 2 diabetic patients (haplotype frequency: 6.3%, <it>P </it>= 0.03; odds ratio [OR]: 14.5; 95% confidence interval [CI]: 1.3- 160.7) at baseline. Over an average follow-up period of 5.7 years, carriers with the risk-conferring haplotype were more likely to develop PAD (<it>P </it>= 0.007; hazard ratio: 6.78; 95% CI: 1.66- 27.6) than were non-carriers. These associations remained significant after adjustment for other risk factors of PAD.</p> <p>Conclusion</p> <p>Our data demonstrate that genetic polymorphism of the <it>SLC2A10 </it>gene is an independent risk factor for PAD in type 2 diabetes.</p