Logical inference approach to relativistic quantum mechanics: derivation of the Klein-Gordon equation

The logical inference approach to quantum theory, proposed earlier [Ann. Phys. 347 (2014) 45-73], is considered in a relativistic setting. It is shown that the Klein-Gordon equation for a massive, charged, and spinless particle derives from the combination of the requirements that the space-time data collected by probing the particle is obtained from the most robust experiment and that on average, the classical relativistic equation of motion of a particle holds

Photo-induced magnetization enhancement in two-dimensional weakly anisotropic Heisenberg magnets

By comparing the photo-induced magnetization dynamics in simple layered systems we show how light-induced modifications of the magnetic anisotropy directly enhance the magnetization. It is observed that the spin precession in (CH3NH3)2CuCl4, initiated by a light pulse, increases in amplitude at the critical temperature TC. The phenomenon is related to the dependence of the critical temperature on the axial magnetic anisotropy. The present results underline the possibility and the importance of the optical modifications of the anisotropy, opening new paths toward the control of the magnetization state for ultrafast memories.Comment: 5 pages, 3 figures, supplementary info as SIr.pd

Multinomial belief networks for healthcare data

Healthcare data from patient or population cohorts are often characterized by sparsity, high missingness and relatively small sample sizes. In addition, being able to quantify uncertainty is often important in a medical context. To address these analytical requirements we propose a deep generative Bayesian model for multinomial count data. We develop a collapsed Gibbs sampling procedure that takes advantage of a series of augmentation relations, inspired by the Zhou\unicode{x2013}Cong\unicode{x2013}Chen model. We visualise the model's ability to identify coherent substructures in the data using a dataset of handwritten digits. We then apply it to a large experimental dataset of DNA mutations in cancer and show that we can identify biologically meaningful clusters of mutational signatures in a fully data-driven way.Comment: 18 pages, 4 figs; supplement: 22 page

Phonon and crystal field excitations in geometrically frustrated rare earth titanates

The phonon and crystal field excitations in several rare earth titanate pyrochlores are investigated. Magnetic measurements on single crystals of Gd2Ti2O7, Tb2Ti2O7, Dy2Ti2O7 and Ho2Ti2O7 are used for characterization, while Raman spectroscopy and terahertz time domain spectroscopy are employed to probe the excitations of the materials. The lattice excitations are found to be analogous across the compounds over the whole temperature range investigated (295-4 K). The resulting full phononic characterization of the R2Ti2O7 pyrochlore structure is then used to identify crystal field excitations observed in the materials. Several crystal field excitations have been observed in Tb2Ti2O7 in Raman spectroscopy for the first time, among which all of the previously reported excitations. The presence of additional crystal field excitations, however, suggests the presence of two inequivalent Tb3+ sites in the low temperature structure. Furthermore, the crystal field level at approximately 13 cm-1 is found to be both Raman and dipole active, indicating broken inversion symmetry in the system and thus undermining its current symmetry interpretation. In addition, evidence is found for a significant crystal field-phonon coupling in Tb2Ti2O7. These findings call for a careful reassessment of the low temperature structure of Tb2Ti2O7, which may serve to improve its theoretical understanding.Comment: 13 pages, 7 figure

Genetic mechanisms of pollution resistance in a marine invertebrate

Pollution is a common stress in the marine environment and one of today's most powerful agents of selection, yet we have little understanding of how anthropogenic toxicants influence mechanisms of adaptation in marine populations. Due to their life history strategies, marine invertebrates are unable to avoid stress and must adapt to variable environments. We examined the genetic basis of pollution resistance across multiple environments using the marine invertebrate, Styela plicata. Gametes were crossed in a quantitative genetic breeding design to enable partitioning of additive genetic variance across a concentration gradient of a common marine pollutant, copper. Hatching success was scored as a measure of stress resistance in copper concentrations of 0, 75, 150, and 350 mu g/L. There was a significant genotype 3 environment interaction in hatching success across copper concentrations. Further analysis using factor analytic modeling confirmed a significant dimension of across-environment genetic variation where the genetic basis of resistance to stress in the first three environments differed from that in the environment of highest copper concentration. A second genetic dimension further differentiated between the genetic basis of resistance to low and high stress environments. These results suggest that marine organisms use different genetic mechanisms to adapt to different levels of pollution and that the level of genetic variation to adapt to intense pollution stresses may be limited

Childhood Characteristics of Adolescent Inpatients with Early-Onset and Adolescent-Onset Disruptive Behavior

Childhood characteristics are associated with life-course-persistent antisocial behavior in epidemiological studies in general population samples. The present study examines this association in an inpatient sample. The purpose is to identify easily measurable childhood characteristics that may guide choice of treatment for adolescent psychiatric inpatients with severe disruptive behavior. Patients (N = 203) were divided into two groups with either early-onset (EO) or adolescent-onset (AO) disruptive behavior, based on ages at which professional care was used for disruptive behavior, referral to special education, and criminal offences. Both groups differed on several childhood characteristics. No gender differences in these characteristics were found. Logistic regression analysis indicated that individuals with grade retention in primary school, childhood impulsive behavior, and a history of physical abuse, had the highest probability of being member of the EO group. These characteristics are reasonably easy to identify, likely apply to other clinical samples as well, and may help clinicians to target their treatment

Reliability of panel-based mutational signatures for immune-checkpoint-inhibition efficacy prediction in non-small cell lung cancer

OBJECTIVES: Mutational signatures (MS) are gaining traction for deriving therapeutic insights for immune checkpoint inhibition (ICI). We asked if MS attributions from comprehensive targeted sequencing assays are reliable enough for predicting ICI efficacy in non-small cell lung cancer (NSCLC).METHODS: Somatic mutations of m = 126 patients were assayed using panel-based sequencing of 523 cancer-related genes. In silico simulations of MS attributions for various panels were performed on a separate dataset of m = 101 whole genome sequenced patients. Non-synonymous mutations were deconvoluted using COSMIC v3.3 signatures and used to test a previously published machine learning classifier.RESULTS: The ICI efficacy predictor performed poorly with an accuracy of 0.51 -0.09 +0.09, average precision of 0.52 -0.11 +0.11, and an area under the receiver operating characteristic curve of 0.50 -0.09 +0.10. Theoretical arguments, experimental data, and in silico simulations pointed to false negative rates (FNR) related to panel size. A secondary effect was observed, where deconvolution of small ensembles of point mutations lead to reconstruction errors and misattributions. CONCLUSION: MS attributions from current targeted panel sequencing are not reliable enough to predict ICI efficacy. We suggest that, for downstream classification tasks in NSCLC, signature attributions be based on whole exome or genome sequencing instead.</p

Patient Referral Patterns and the Spread of Hospital-Acquired Infections through National Health Care Networks

Rates of hospital-acquired infections, such as methicillin-resistant Staphylococcus aureus (MRSA), are increasingly used as quality indicators for hospital hygiene. Alternatively, these rates may vary between hospitals, because hospitals differ in admission and referral of potentially colonized patients. We assessed if different referral patterns between hospitals in health care networks can influence rates of hospital-acquired infections like MRSA. We used the Dutch medical registration of 2004 to measure the connectedness between hospitals. This allowed us to reconstruct the network of hospitals in the Netherlands. We used mathematical models to assess the effect of different patient referral patterns on the potential spread of hospital-acquired infections between hospitals, and between categories of hospitals (University medical centers, top clinical hospitals and general hospitals). University hospitals have a higher number of shared patients than teaching or general hospitals, and are therefore more likely to be among the first to receive colonized patients. Moreover, as the network is directional towards university hospitals, they have a higher prevalence, even when infection control measures are equally effective in all hospitals. Patient referral patterns have a profound effect on the spread of health care-associated infections like hospital-acquired MRSA. The MRSA prevalence therefore differs between hospitals with the position of each hospital within the health care network. Any comparison of MRSA rates between hospitals, as a benchmark for hospital hygiene, should therefore take the position of a hospital within the network into account

Monitoring the spread of meticillin-resistant Staphylococcus aureus in The Netherlands from a reference laboratory perspective

SummaryBackgroundIn The Netherlands, efforts to control meticillin-resistant Staphylococcus aureus (MRSA) in hospitals have been largely successful due to stringent screening of patients on admission and isolation of those that fall into defined risk categories. However, Dutch hospitals are not free of MRSA, and a considerable number of cases are found that do not belong to any of the risk categories. Some of these may be due to undetected nosocomial transmission, whereas others may be introduced from unknown reservoirs.AimIdentifying multi-institutional clusters of MRSA isolates to estimate the contribution of potential unobserved reservoirs in The Netherlands.MethodsWe applied a clustering algorithm that combines time, place, and genetics to routine data available for all MRSA isolates submitted to the Dutch Staphylococcal Reference Laboratory between 2008 and 2011 in order to map the geo-temporal distribution of MRSA clonal lineages in The Netherlands.FindingsOf the 2966 isolates lacking obvious risk factors, 579 were part of geo-temporal clusters, whereas 2387 were classified as MRSA of unknown origin (MUOs). We also observed marked differences in the proportion of isolates that belonged to geo-temporal clusters between specific multi-locus variable number of tandem repeat analysis (MLVA) clonal complexes, indicating lineage-specific transmissibility. The majority of clustered isolates (74%) were present in multi-institutional clusters.ConclusionThe frequency of MRSA of unknown origin among patients lacking obvious risk factors is an indication of a largely undefined extra-institutional but genetically highly diverse reservoir. Efforts to understand the emergence and spread of high-risk clones require the pooling of routine epidemiological information and typing data into central databases