Theory of defect-induced crystal field perturbations in rare earth magnets

We present a theory describing the single-ion anisotropy of rare earth (RE) magnets in the presence of point defects. Taking the RE-lean 1:12 magnet class as a prototype, we use first-principles calculations to show how the introduction of Ti substitutions into SmFe$_{12}$ perturbs the crystal field, generating new coefficients due to the lower symmetry of the RE environment. We then demonstrate that these perturbations can be described extremely efficiently using a screened point charge model. We provide analytical expressions for the anisotropy energy which can be straightforwardly implemented in atomistic spin dynamics simulations, meaning that such simulations can be carried out for an arbitrary arrangement of point defects. The significant crystal field perturbations calculated here demonstrate that a sample which is single-phase from a structural point of view can nonetheless have a dramatically varying anisotropy profile at the atomistic level if there is compositional disorder, which may influence localized magnetic objects like domain walls or skyrmions.Comment: 14 pages, 3 figure

Modularity of Escherichia coli sRNA regulation revealed by sRNA-target and protein network analysis

<p>Abstract</p> <p>Background</p> <p>sRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be <it>trans</it>-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in <it>Escherichia coli </it>to shed light on the global correlation between sRNA regulation and cellular control networks.</p> <p>Results</p> <p>The analysis of sRNA targets in terms of their networks showed that some specific network properties could be identified. In protein-protein interaction network, sRNA targets tend to occupy more central positions (higher closeness centrality, <it>p-val </it>= 0.022) and more cliquish (larger clustering coefficient, <it>p-val </it>= 0.037). The targets of the same sRNA tend to form a network module (shorter characteristic path length, <it>p-val </it>= 0.015; larger density, <it>p-val </it>= 0.019; higher in-degree ratio, <it>p-val </it>= 0.009). Using the transcription-regulatory network, sRNA targets tend to be under multiple regulation (higher indegree, <it>p-val </it>= 0.013) and the targets usually are important to the transfer of regulatory signals (higher betweenness, <it>p-val </it>= 0.012). As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, <it>p-val </it>= 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, <it>p-val </it>= 0.023). However, after incorporating information on predicted sRNAs and down-stream targets, the results are not as clear-cut, but the overall network modularity is still evident.</p> <p>Conclusions</p> <p>Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study. Namely, the sRNA targets show close interaction and forms a closely knit network module for both the protein-protein interaction and the transcription-regulatory networks. Thus, targets of the same sRNA work in a concerted way toward a specific goal. In addition, in the transcription-regulatory network, sRNA targets act as "multiplexor", accepting regulatory control from multiple sources and acting accordingly. Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.</p

Theory of defect-induced crystal field perturbations in rare-earth magnets

We present a theory describing the single-ion anisotropy of rare-earth (RE) magnets in the presence of point defects. Taking the RE-lean 1∶12 magnet class as a prototype, we use first-principles calculations to show how the introduction of Ti substitutions into SmFe_{12} perturbs the crystal field, generating new coefficients due to the lower symmetry of the RE environment. We then demonstrate that these perturbations can be described extremely efficiently using a screened point charge model. We provide analytical expressions for the anisotropy energy that can be straightforwardly implemented in atomistic spin dynamics simulations, meaning that such simulations can be carried out for an arbitrary arrangement of point defects. The significant crystal field perturbations calculated here demonstrate that a sample that is single phase from a structural point of view can nonetheless have a dramatically varying anisotropy profile at the atomistic level if there is compositional disorder, which may influence localized magnetic objects like domain walls or skyrmions

The Effect of Student-Run Vision Screenings on Ophthalmic Education and Recognition of Visual Impairment

Objective: For many in the United States, standard health insurance does not cover eyecare, leading to lapses in care and exacerbations of pre-existing conditions. Touro College of Osteopathic Medicine (TouroCOM) recognizes the importance of ocular health and offers the opportunity to engage the community through student-run vision screenings. This study aims to assess the effect of medical student-run vision screenings in supplementing pre-clinical education and to review health fair data on common vision pathologies seen in Harlem, New York.Methods: Pre- and post-surveys were administered to medical student volunteers to assess their comfort in performing a basic vision screening. Training was given in regards to screening protocols and applied at health fairs.Results: 90% of medical students (n=20) indicated discomfort in performing a basic vision screening when solely relying on their preclinical coursework. In comparison, after a training session and use during a health fair, 100% (n=20) indicated that they were comfortable with performing a vision screening.  60% of health fair participants (n=193) met referral criteria in requiring further testing or follow-up care. 100% of participants had some degree of refractive error, with 6% (n=7) having concomitant color vision abnormality and 9% (n=11) with macular abnormalities.Conclusion: Osteopathic medical students are better equipped to perform basic vision screenings and recognize visual disease with additional training and practice at health fairs. This engagement allows for early clinical experience, osteopathic outreach, and interprofessionalism. Furthermore, this provides an opportunity for community members to receive information that may guide future health decisions

Tet1 and Tet2 Regulate 5-Hydroxymethylcytosine Production and Cell Lineage Specification in Mouse Embryonic Stem Cells

SummaryTET family enzymes convert 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) in DNA. Here, we show that Tet1 and Tet2 are Oct4-regulated enzymes that together sustain 5hmC in mouse embryonic stem cells (ESCs) and are induced concomitantly with 5hmC during reprogramming of fibroblasts to induced pluripotent stem cells. ESCs depleted of Tet1 by RNAi show diminished expression of the Nodal antagonist Lefty1 and display hyperactive Nodal signaling and skewed differentiation into the endoderm-mesoderm lineage in embryoid bodies in vitro. In Fgf4- and heparin-supplemented culture conditions, Tet1-depleted ESCs activate the trophoblast stem cell lineage determinant Elf5 and can colonize the placenta in midgestation embryo chimeras. Consistent with these findings, Tet1-depleted ESCs form aggressive hemorrhagic teratomas with increased endoderm, reduced neuroectoderm, and ectopic appearance of trophoblastic giant cells. Thus, 5hmC is an epigenetic modification associated with the pluripotent state, and Tet1 functions to regulate the lineage differentiation potential of ESCs

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

IMPORTANCE Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. OBJECTIVE To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. DESIGN, SETTING, AND PARTICIPANTS A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. EXPOSURES Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. MAIN OUTCOMES AND MEASURES Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. RESULTS Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2%[142 180] of the cohort), medium-risk (18.6%[34 579] of the cohort), and high-risk (5.2%[9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. CONCLUSIONS AND RELEVANCE Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.NIH/National Institute on Drug Abuse [R01DA044985]; Pharmaceutical Research and Manufacturers of America Foundation Research Starter AwardOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Wolbachia and DNA barcoding insects: patterns, potential and problems

Wolbachia is a genus of bacterial endosymbionts that impacts the breeding systems of their hosts. Wolbachia can confuse the patterns of mitochondrial variation, including DNA barcodes, because it influences the pathways through which mitochondria are inherited. We examined the extent to which these endosymbionts are detected in routine DNA barcoding, assessed their impact upon the insect sequence divergence and identification accuracy, and considered the variation present in Wolbachia COI. Using both standard PCR assays (Wolbachia surface coding protein – wsp), and bacterial COI fragments we found evidence of Wolbachia in insect total genomic extracts created for DNA barcoding library construction. When >2 million insect COI trace files were examined on the Barcode of Life Datasystem (BOLD) Wolbachia COI was present in 0.16% of the cases. It is possible to generate Wolbachia COI using standard insect primers; however, that amplicon was never confused with the COI of the host. Wolbachia alleles recovered were predominantly Supergroup A and were broadly distributed geographically and phylogenetically. We conclude that the presence of the Wolbachia DNA in total genomic extracts made from insects is unlikely to compromise the accuracy of the DNA barcode library; in fact, the ability to query this DNA library (the database and the extracts) for endosymbionts is one of the ancillary benefits of such a large scale endeavor – for which we provide several examples. It is our conclusion that regular assays for Wolbachia presence and type can, and should, be adopted by large scale insect barcoding initiatives. While COI is one of the five multi-locus sequence typing (MLST) genes used for categorizing Wolbachia, there is limited overlap with the eukaryotic DNA barcode region

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition