Origin of the Resistivity Anisotropy in the Nematic Phase of FeSe

The in-plane resistivity anisotropy is studied in strain-detwinned single crystals of FeSe. In contrast to other iron-based superconductors, FeSe does not develop long-range magnetic order below the tetragonal-to-orthorhombic transition at Ts≈90  K. This allows for the disentanglement of the contributions to the resistivity anisotropy due to nematic and magnetic orders. Comparing direct transport and elastoresistivity measurements, we extract the intrinsic resistivity anisotropy of strain-free samples. The anisotropy peaks slightly below Ts and decreases to nearly zero on cooling down to the superconducting transition. This behavior is consistent with a scenario in which the in-plane resistivity anisotropy is dominated by inelastic scattering by anisotropic spin fluctuations

Classical Mus musculus Igκ Enhancers Support Transcription but not High Level Somatic Hypermutation from a V-Lambda Promoter in Chicken DT40 Cells

Somatic hypermutation (SHM) of immunoglobulin genes is initiated by activation-induced cytidine deaminase (AID) in activated B cells. This process is strictly dependent on transcription. Hence, cis-acting transcriptional control elements have been proposed to target SHM to immunoglobulin loci. The Mus musculus Igκ locus is regulated by the intronic enhancer (iE/MAR) and the 3′ enhancer (3′E), and multiple studies using transgenic and knock-out approaches in mice and cell lines have reported somewhat contradictory results about the function of these enhancers in AID-mediated sequence diversification. Here we show that the M. musculus iE/MAR and 3′E elements are active solely as transcriptional enhancer when placed in the context of the IGL locus in Gallus gallus DT40 cells, but they are very inefficient in targeting AID-mediated mutation events to this locus. This suggests that either key components of the cis-regulatory targeting elements reside outside the murine Igκ transcriptional enhancer sequences, or that the targeting of AID activity to Ig loci occurs by largely species-specific mechanisms

Thin Film PZT-Based PMUT Arrays for Deterministic Particle Manipulation

Lead zirconate titanate (PZT) based piezoelectric micromachined ultrasonic transducers (PMUTs) for particle manipulation applications were designed, fabricated, characterized and tested. The PMUTs had a diaphragm diameter of 60 lm, a resonant frequency of ∼ 8 MHz and an operational bandwidth of 62.5%. Acoustic pressure output in water was 9.5 kPa at 7.5 mm distance from a PMUT element excited with a unipolar waveform at 5 Vpp. The element consisted of 20 diaphragms connected electrically in parallel. Particle trapping of 4 lm silica beads was shown to be possible with 5 Vpp unipolar excitation. Trapping of multiple beads by a single element and deterministic control of particles via acoustophoresis without the assistance of microfluidic flow were demonstrated. It was found that the particles move towards diaphragm areas of highest pressure, in agreement with literature and simulations. Unique bead patterns were generated at different driving frequencies and were formed at frequencies up to 60 MHz, much higher than the operational bandwidth. Levitation planes were generated above 30 MHz driving frequency

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets

Suppression of magnetic order in CaCo 1.86 As 2 with Fe substitution: Magnetization, neutron diffraction, and x-ray diffraction studies of Ca ( Co 1 − x Fe x ) y As 2

Magnetization, neutron diffraction, and high-energy x-ray diffraction results for Sn-flux grown single-crystal samples of Ca(Co$_{1-x}$Fe$_{x}$)$_{y}$As$_{2}$, $0\leq x\leq1$, $1.86\leq y \leq 2$, are presented and reveal that A-type antiferromagnetic order, with ordered moments lying along the $c$ axis, persists for $x\lesssim0.12(1)$. The antiferromagnetic order is smoothly suppressed with increasing $x$, with both the ordered moment and N\'{e}el temperature linearly decreasing. Stripe-type antiferromagnetic order does not occur for $x\leq0.25$, nor does ferromagnetic order for $x$ up to at least $x=0.104$, and a smooth crossover from the collapsed-tetragonal (cT) phase of CaCo$_{1.86}$As$_{2}$ to the tetragonal (T) phase of CaFe$_{2}$As$_{2}$ occurs. These results suggest that hole doping CaCo$_{1.86}$As$_{2}$ has a less dramatic effect on the magnetism and structure than steric effects due to substituting Sr for Ca.Comment: 12 pages, 16 Figure

Local algorithms : Self-stabilization on speed

Non peer reviewe

Empirical comparison of cross-platform normalization methods for gene expression data

<p>Abstract</p> <p>Background</p> <p>Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.</p> <p>Results</p> <p>Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.</p> <p>Conclusions</p> <p>Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at <url>http://alborz.sdsu.edu/conor</url> and is available from CRAN.</p