Evaluation of cytosine DNA methylation of the Biomphalaria glabratahe at shock protein 70 locus after biological and physiological stresses

Deoxyribonucleic (DNA) methylation is one of the widespread epigenetic modifications of genomic DNA, and has been postulated to be a predisposing influence on disease onset and infections. The ability to quantify differences in DNA methylation between the genomes of normal vs.stressed Biomphalaria glabrata would help to profile changes potentially linked to resistance to Schistosoma mansoni infection. Thus, this study sought to measure differences in cytosine DNA methylation of various B. glabrata tissues responding over time to either biological or physiological stresses (S. mansoni exposure vs. heat shock). Here, this study measured DNA methylation at the B. glabrataheat shock protein 70 (Bg-hsp 70) intragenic regionto profile the regions methylome with a simple and cost-effective method. The study found DNA hypomethylation of the Bg-hsp 70 region occurs in the snails in response to both stressors; heat shock and parasite exposure, however, overall DNA hypomethylation after heat shock was similar among the tissues examined. In contrast, DNA methylation remained suppressed for up to 5 h when snails were responding to stress from parasite exposure. In parasite exposed snails, the levels of Bg-hsp 70 methylation in whole body, head foot and ovotestis decreased from 30 min to 2 h, and the reduction persisted in the hepatopancreas for up to 5 h. The DNA hypomethylation of the Bg-hsp 70 intragenic region correlated negatively with the level of Bg-hsp 70 mRNA in infected snails but not in thermally stressed snails. From this study results, the study conclude that the hepatopancreas is the most active host organs in terms of differential DNA methylation events following parasite infection. Also, from these data the study postulate that different epigenetic mechanisms underlie, Bg-hsp70 gene regulation in this snails while responding to stress due either to parasite exposure or heat shock

One-Component Order Parameter in URu2_2Si2_2 Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning

The unusual correlated state that emerges in URu$_2$Si$_2$ below T$_{HO}$ = 17.5 K is known as "hidden order" because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are "hidden". We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T$_{HO}$. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems

Multifaceted machine learning of competing orders in disordered interacting systems

While the nonperturbative interaction effects in the fractional quantum Hall regime can be readily simulated through exact diagonalization, it has been challenging to establish a suitable diagnostic that can label different phases in the presence of competing interactions and disorder. Here we introduce a multifaceted framework using a simple artificial neural network (ANN) to detect defining features of a fractional quantum Hall state, a charge-density-wave state and a localized state using the entanglement spectra and charge density as independent input. We consider the competing effects of a perturbing interaction (l=1 pseudopotential ΔV1), a disorder potential W, and the Coulomb interaction to the system at filling fraction ν=1/3. Our phase diagram benchmarks well against previous estimates of the phase boundary along the axes of our phase diagram where other measures exist. Moreover, exploring the entire two-dimensional phase diagram, we establish the robustness of the fractional quantum Hall state and map out the charge-density-wave microemulsion phase wherein droplets of the charge-density-wave region appear before the charge density wave is completely disordered. Hence we establish that the ANN can access and learn the defining traits of topological as well as broken symmetry phases using multifaceted inputs of entanglement spectra and charge density

No agreement of mixed venous and central venous saturation in sepsis, independent of sepsis origin

Introduction: Controversy remains regarding the relationship between central venous saturation (ScvO(2)) and mixed venous saturation (SvO(2)) and their use and interchangeability in patients with sepsis or septic shock. We tested the hypothesis that ScvO(2) does not reliably predict SvO(2) in sepsis. Additionally we looked at the influence of the source (splanchnic or non-splanchnic) of sepsis on this relationship. Methods: In this prospective observational two-center study we concurrently determined ScvO(2) and SvO(2) in a group of 53 patients with severe sepsis during the first 24 hours after admission to the intensive care units in 2 Dutch hospitals. We assessed correlation and agreement of ScvO(2) and SvO(2), including the difference, i.e. the gradient, between ScvO(2) and SvO(2) (ScvO(2) -SvO(2)). Additionally, we compared the mean differences between ScvO(2) and SvO(2) of both splanchnic and non-splanchnic group. Results: A total of 265 paired blood samples were obtained. ScvO(2) overestimated SvO(2) by less than 5% with wide limits of agreement. For changes in ScvO(2) and SvO(2) results were similar. The distribution of the (ScvO(2) - SvO(2)) (< 0 or >= 0) was similar in survivors and nonsurvivors. The mean (ScvO(2) - SvO(2)) in the splanchnic group was similar to the mean (ScvO(2) - SvO(2)) in the non-splanchnic group (0.8 +/- 3.9% vs. 2.5 +/- 6.2%; P = 0.30). O2ER (P = 0.23) and its predictive value for outcome (P = 0.20) were similar in both groups. Conclusions: ScvO(2) does not reliably predict SvO(2) in patients with severe sepsis. The trend of ScvO(2) is not superior to the absolute value in this context. A positive difference (ScvO(2) -SvO(2)) is not associated with improved outcome

Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern x-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big data sets when a comprehensive analysis is beyond human reach. We report the development of a novel unsupervised machine learning approach, XRD Temperature Clustering (X-TEC), that can automatically extract charge density wave (CDW) order parameters and detect intra-unit cell (IUC) ordering and its fluctuations from a series of high-volume X-ray diffraction (XRD) measurements taken at multiple temperatures. We apply X-TEC to XRD data on a quasi-skutterudite family of materials, (Ca$_x$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$, where a quantum critical point arising from charge order is observed as a function of Ca concentration. We further apply X-TEC to XRD data on the pyrochlore metal, Cd$_2$Re$_2$O$_7$, to investigate its two much debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC-revealed selection rule that the Cd and Re displacements are approximately equal in amplitude, but out of phase. This discovery reveals a previously unknown involvement of $5d^2$ Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in-operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on-the-fly

Pulsed chemical vapor deposition of conformal GeSe for application as an OTS selector

The ovonic threshold switch (OTS) selector based on the voltage snapback of amorphous chalcogenides has received tremendous attention as it provides several desirable characteristics such as bidirectional switching, a controllable threshold voltage, high drive currents, and low leakage currents. GeSe is a well-known OTS selector that fulfills all the requirements imposed by future high-density storage class memories. Here, we report on pulsed chemical vapor deposition (CVD) of amorphous GeSe by using GeCl2 center dot C4H8O2 as a Ge source and two different Se sources namely bis-trimethylsilylselenide ((CH3)(3)Si)(2)Se (TMS)(2)Se and bis-triethylsilylselenide ((C2H5)(3)Si)(2)Se (TES)(2)Se. We utilized total reflection X-ray fluorescence (TXRF) to study the kinetics of precursor adsorption on the Si substrate. GeCl2 center dot C4H8O2 precursor adsorption on a 300 mm Si substrate showed under-dosing due to limited precursor supply. On the other hand, the Se precursor adsorption is limited by low reaction efficiency as we learned from a better within-wafer uniformity. Se precursors need Cl sites (from Ge precursor) for precursor ligand exchange reactions. Adsorption of (TMS)(2)Se is found to be much faster than (TES)(2)Se on a precoated GeClx layer. Atomic layer deposition (ALD) tests with GeCl2 center dot C4H8O2 and (TMS)(2)Se revealed that the growth per cycle (GPC) decreases with the introduction of purge steps in the ALD cycle, whereas a higher GPC is obtained in pulsed-CVD mode without purges. Based on this basic understanding of the process, we developed a pulsed CVD growth recipe (GPC = 0.3 angstrom per cycle) of GeSe using GeCl2 center dot C4H8O2 and (TMS)(2)Se at a reactor temperature of 70 degrees C. The 20 nm GeSe layer is amorphous and stoichiometric with traces of chlorine and carbon impurities. The film has a roughness of similar to 0.3 nm and it starts to crystallize at a temperature of similar to 370 degrees C. GeSe grown on 3D test structures showed excellent film conformality

The Grizzly, April 4, 2000

Future Changes in Store for UC • Phi Beta Kappa Speaker Set to Arrive on April 6th • International Round Table Important for Student Input • Mail Boxes, Etc. the Place for all Your Copying Needs • Valedictorian and Salutatorian Announced • Letters to the Editor: Debate Disappointment; Bringing Culture to the Grizzly • Design Versus Darwinism, a New Twist on an Old Debate • Problems With Housing? Maybe it\u27s Something in the Air • Music Review: Jimmy Thackery and the Drivers • Softball Improves to 16-4 • UC Baseball Begins Defense of CC Title • Rocky Start for Ursinus Tennis • UC Lax: Prepared to Take the Challenge • Wrestling with the Books: A Full Pin • CC Honors • UC Tumblers Top Off Season at NCAA Championships • Ursinus Track Tackles Widener • Sports Profile: Matt Wiatrakhttps://digitalcommons.ursinus.edu/grizzlynews/1464/thumbnail.jp