Magnetic properties of σ\sigma-FeCr alloy as calculated with the charge and spin self-consistent KKR(CPA) method

Magnetic properties of a $\sigma-$Fe$_{16}$Cr$_{14}$ alloy calculated with the charge and spin self- consistent Korringa-Kohn-Rostoker (KKR) and combined with coherent potential approximation (KKR-CPA) methods are reported. Non-magnetic state as well as various magnetic orderings were considered, i.e. ferromagnetic (FM) and more complex anti-parallel (called APM) arrangements for selected sublattices, as follows from the symmetry analysis. It has been shown that the Stoner criterion applied to non-magnetic density of states at the Fermi energy, $E_F$ is satisfied for Fe atoms situated on all five lattice sites, while it is not fulfilled for all Cr atoms. In FM and APM states, the values of magnetic moments on Fe atoms occupying various sites are dispersed between 0 and 2.5 $\mu_B$, and they are proportional to the number of Fe atoms in the nearest-neighbor shell. Magnetic moments of Cr atoms havin much smaller values were found to be coupled antiparallel to those of Fe atoms. The average value of the magnetic moment per atom was found to be $=0.55 \mu_B$ that is by a factor of 4 larger than the experimental value found for a $\sigma-$Fe$_{0.538}$Cr$_{0.462}$ sample. Conversely, admitting an anti- parallel ordering (APM model) on atoms situated on C and D sites, according to the group theory and symmetry analysis results, yielded a substantial reduction of to 0.20 $\mu_B$. Further diminution of to 0.15 $\mu_B$, which is very close to the experimental value of 0.14 $\mu_B$, has been achieved with the KKR-CPA calculations by considering a chemical disorder on sites B, C and D

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Tracking drug-resistant Streptococcus pneumoniae in Oregon: an alternative surveillance method.

With the emergence of drug-resistant Streptococcus pneumoniae, community-specific antimicrobial susceptibility patterns have become valuable determinants of empiric therapy for S. pneumoniae infections. Traditionally, these patterns are tracked by active surveillance for invasive disease, collection of isolates, and centralized susceptibility testing. We investigated whether a simpler and less expensive method aggregating existing hospital antibiograms--could provide community-specific antimicrobial susceptibility data. We compared 1996 active surveillance data with antibiogram data from hospital laboratories in Portland, Oregon. Of the 178 S. pneumoniae active surveillance isolates, 153 (86% [95% confidence interval (CI) = 80% to 91%]) were susceptible to penicillin. Of the 1,092 aggregated isolates used by hospitals to generate antibiograms, 921 (84% [95% CI = 82%-87%]) were susceptible to penicillin. With the exception of one hospital's erythromycin susceptibility results, hospital-specific S. pneumoniae susceptibilities to penicillin, cefotaxime, trimethoprim-sulfamethoxazole, and erythromycin from the two methods were statistically comparable. Although yielding fewer data than active surveillance, antibiograms provided accurate, community-specific drug-resistant S. pneumoniae data in Oregon

Prediction of a neuropeptidome for the eyestalk ganglia of the lobster Homarus americanus using a tissue-specific de novo assembled transcriptome

In silico transcriptome mining is a powerful tool for crustacean peptidome prediction. Using homology-based BLAST searches and a simple bioinformatics workflow, large peptidomes have recently been predicted for a variety of crustaceans, including the lobster, Homarus americanus. Interestingly, no in silico studies have been conducted on the eyestalk ganglia (lamina ganglionaris, medulla externa, medulla interna and medulla terminalis) of the lobster, although the eyestalk is the location of a major neuroendocrine complex, i.e., the X-organ-sinus gland system. Here, an H. americanus eyestalk ganglia-specific transcriptome was produced using the de novo assembler Trinity. This transcriptome was generated from 130,973,220 Illumina reads and consists of 147,542 unique contigs. Eighty-nine neuropeptide-encoding transcripts were identified from this dataset, allowing for the deduction of 62 distinct pre/preprohormones. Two hundred sixty-two neuropeptides were predicted from this set of precursors; the peptides include members of the adipokinetic hormone-corazonin-like peptide, allatostatin A, allatostatin B, allatostatin C, bursicon α, CCHamide, corazonin, crustacean cardioactive peptide, crustacean hyperglycemic hormone (CHH), CHH precursor-related peptide, diuretic hormone 31, diuretic hormone 44, eclosion hormone, elevenin, FMRFamide-like peptide, glycoprotein hormone α2, glycoprotein hormone β5, GSEFLamide, intocin, leucokinin, molt-inhibiting hormone, myosuppressin, neuroparsin, neuropeptide F, orcokinin, orcomyotropin, pigment dispersing hormone, proctolin, pyrokinin, red pigment concentrating hormone, RYamide, short neuropeptide F, SIFamide, sulfakinin, tachykinin-related peptide and trissin families. The predicted peptides expand the H. americanus eyestalk ganglia neuropeptidome approximately 7-fold, and include 78 peptides new to the lobster. The transcriptome and predicted neuropeptidome described here provide new resources for investigating peptidergic signaling within/from the lobster eyestalk ganglia

Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both

Enhanced Out-of-plane Emission of K+ Mesons observed in Au+Au Collisions at 1 AGeV

The azimuthal angular distribution of K+ mesons has been measured in Au + Au collisions at 1 AGeV. In peripheral and semi-central collisions, K+ mesons preferentially are emitted perpendicular to the reaction plane. The strength of the azimuthal anisotropy of K+ emission is comparable to the one of pions. No in-plane flow was found for K+ mesons near projectile and target rapidity.Comment: Accepted for publication in Phys. Rev.Let

An Environmental Scan of Mindfulness-Based Interventions on University and College Campuses: A Research Note

The purpose of this research note is to provide readers with an understanding of the diverse types of student mental health interventions that are being offered on North American universities/ colleges broken down into two types of interventions: (1) traditional, or non-mindfulness-based interventions, and (2) mindfulness-based interventions. Data were collected, organized, and synthesized during the first 5 months of 2016 (via a simple Google searches) for all North American universities/colleges that offered their students mental health interventions on their campuses. Traditional, or non-mindfulness-based interventions remain widely in use on university/college campuses and include: prevention and outreach, support groups and workshops, individual counseling, and self-help. Mindfulness-based interventions, although less widely available, include: mindfulness-based cognitive therapy, mindfulness- based stress reduction, guided meditations and yoga, compassion training, mindfulness-based technology, and mindful eating. There is an abundance of data that seem to indicate that colleges/universities are increasing the mental health interventions they offer to their students. In addition, the use of mindfulness- based interventions (a sub-set of mental health interventions) seems to be being used with an increasing frequency