On the fluid-fluid phase separation in charged-stabilized colloidal suspensions

We develop a thermodynamic description of particles held at a fixed surface potential. This system is of particular interest in view of the continuing controversy over the possibility of a fluid-fluid phase separation in aqueous colloidal suspensions with monovalent counterions. The condition of fixed surface potential allows in a natural way to account for the colloidal charge renormalization. In a first approach, we assess the importance of the so called ``volume terms'', and find that in the absence of salt, charge renormalization is sufficient to stabilize suspension against a fluid-fluid phase separation. Presence of salt, on the other hand, is found to lead to an instability. A very strong dependence on the approximations used, however, puts the reality of this phase transition in a serious doubt. To further understand the nature of the instability we next study a Jellium-like approximation, which does not lead to a phase separation and produces a relatively accurate analytical equation of state for a deionized suspensions of highly charged colloidal spheres. A critical analysis of various theories of strongly asymmetric electrolytes is presented to asses their reliability as compared to the Monte Carlo simulations

Genetic analysis of cardiac SCN5A Gene in Iranian patients with hereditary cardiac arrhythmias.

SCN5A encodes alpha subunit of the major sodium channel (Nav1.5) in human cardiac tissue. Malfunction of this cardiac sodium channel is associated with a variety of cardiac arrhythmias and myocardial inherited diseases. Fifty-three members from three families each diagnosed with long-QT syndrome type 3 (LQTS3), Brugada syndrome (BrS), or sick sinus syndrome (SSS) were included in this observational, cross-sectional study. In this study, we analyzed the sequences of coding region of the SCN5A gene. Eleven members of the LQTS family (39%) showed p.Gln1507-Lys1508-Pro1509del mutation, 8 of BrS family (50%) showed p.Arg222Ter nonsense mutation, and 5 of 9 SSS family members (55%) showed a novel p.Met1498Arg mutation in the SCN5A gene. p.Gln1507-Lys1508-Pro1509del mutation, p.Arg222Ter nonsense mutation, and p.Met1498Arg in LQTS, BrS, and SSS, respectively, are reported for the first time in the Iranian population. Information regarding underlying genetic defects would be necessary for verifying certain clinically diagnosed arrhythmia types, carrier screening in affected families, and more precise therapy of the patients are required

YIELD AND YIELD ATTRIBUTES OF RAPESEED AS INFLUENCED BY DATE OF PLANTING

ABSTRACT (Ghosh and Chatterjee, 1988) . The average yield of mustard in this country is 739 kg/ha whereas the world average yield of mustard is 1575 kg/h

Genetic Diversity and Phylogenetic Analysis of South-East Asian Duck Populations Based on the mtDNA D-loop Sequences

The maternally inherited mitochondrial DNA (mtDNA) D–loop region is widely used for exploring genetic relationships and for investigating the origin of various animal species. Currently, domestic ducks play an important role in animal protein supply. In this study, partial mtDNA D–loop sequences were obtained from 145 samples belonging to six South-East Asian duck populations and commercial duck population. All these populations were closely related to the mallard duck (Anas platyrhynchos), as indicated by their mean overall genetic distance. Sixteen nucleotide substitutions were identified in sequence analyses allowing the distinction of 28 haplotypes. Around 42.76% of the duck sequences were classified as Hap_02, which completely matched with Anas platyrhynchos duck species. The neighbor-joining phylogenetic tree also revealed that South-East Asian duck populations were closely related to Anas platyrhynchos. Network profiles were also traced using the 28 haplotypes. Overall, results showed that those duck populations D-loop haplotypes were shared between several duck breeds from Korea and Bangladesh sub continental regions. Therefore, these results confirmed that South-East Asian domestic duck populations have been domesticated from Anas platyrhynchos duck as the maternal origins

Experimental Study on the Low-velocity Impact Behavior of Foam-core Sandwich Panels

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97064/1/AIAA2012-1701.pd

Circulating Mucosal Associated Invariant T Cells Are Activated in Vibrio cholerae O1 Infection and Associated with Lipopolysaccharide Antibody Responses

Background: Mucosal Associated Invariant T (MAIT) cells are innate-like T cells found in abundance in the intestinal mucosa, and are thought to play a role in bridging the innate-adaptive interface. Methods: We measured MAIT cell frequencies and antibody responses in blood from patients presenting with culture-confirmed severe cholera to a hospital in Dhaka, Bangladesh at days 2, 7, 30, and 90 of illness. Results: We found that MAIT (CD3+CD4−CD161hiVα7.2+) cells were maximally activated at day 7 after onset of cholera. In adult patients, MAIT frequencies did not change over time, whereas in child patients, MAITs were significantly decreased at day 7, and this decrease persisted to day 90. Fold changes in MAIT frequency correlated with increases in LPS IgA and IgG, but not LPS IgM nor antibody responses to cholera toxin B subunit. Conclusions: In the acute phase of cholera, MAIT cells are activated, depleted from the periphery, and as part of the innate response against V. cholerae infection, are possibly involved in mechanisms underlying class switching of antibody responses to T cell-independent antigens

Cryptosporidium Priming Is More Effective than Vaccine for Protection against Cryptosporidiosis in a Murine Protein Malnutrition Model

Cryptosporidium is a major cause of severe diarrhea, especially in malnourished children. Using a murine model of C. parvum oocyst challenge that recapitulates clinical features of severe cryptosporidiosis during malnutrition, we interrogated the effect of protein malnutrition (PM) on primary and secondary responses to C. parvum challenge, and tested the differential ability of mucosal priming strategies to overcome the PM-induced susceptibility. We determined that while PM fundamentally alters systemic and mucosal primary immune responses to Cryptosporidium, priming with C. parvum (106 oocysts) provides robust protective immunity against re-challenge despite ongoing PM. C. parvum priming restores mucosal Th1-type effectors (CD3+CD8+CD103+ T-cells) and cytokines (IFNγ, and IL12p40) that otherwise decrease with ongoing PM. Vaccination strategies with Cryptosporidium antigens expressed in the S. Typhi vector 908htr, however, do not enhance Th1-type responses to C. parvum challenge during PM, even though vaccination strongly boosts immunity in challenged fully nourished hosts. Remote non-specific exposures to the attenuated S. Typhi vector alone or the TLR9 agonist CpG ODN-1668 can partially attenuate C. parvum severity during PM, but neither as effectively as viable C. parvum priming. We conclude that although PM interferes with basal and vaccine-boosted immune responses to C. parvum, sustained reductions in disease severity are possible through mucosal activators of host defenses, and specifically C. parvum priming can elicit impressively robust Th1-type protective immunity despite ongoing protein malnutrition. These findings add insight into potential correlates of Cryptosporidium immunity and future vaccine strategies in malnourished children

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods