Exploring out-of-equilibrium quantum magnetism and thermalization in a spin-3 many-body dipolar lattice system

Understanding quantum thermalization through entanglement build-up in isolated quantum systems addresses fundamental questions on how unitary dynamics connects to statistical physics. Here, we study the spin dynamics and approach towards local thermal equilibrium of a macroscopic ensemble of S = 3 spins prepared in a pure coherent spin state, tilted compared to the magnetic field, under the effect of magnetic dipole-dipole interactions. The experiment uses a unit filled array of 104 chromium atoms in a three dimensional optical lattice, realizing the spin-3 XXZ Heisenberg model. The buildup of quantum correlation during the dynamics, especially as the angle approaches pi/2, is supported by comparison with an improved numerical quantum phase-space method and further confirmed by the observation that our isolated system thermalizes under its own dynamics, reaching a steady state consistent with the one extracted from a thermal ensemble with a temperature dictated from the system's energy. This indicates a scenario of quantum thermalization which is tied to the growth of entanglement entropy. Although direct experimental measurements of the Renyi entropy in our macroscopic system are unfeasible, the excellent agreement with the theory, which can compute this entropy, does indicate entanglement build-up.Comment: 12 figure

Comparison of gene expression profiles in core biopsies and corresponding surgical breast cancer samples

INTRODUCTION: Gene expression profiling has been successfully used to classify breast cancer into clinically distinct subtypes, and to predict the risk of recurrence and treatment response. The aim of this study was to investigate whether the gene expression profile (GEP) detected in a core biopsy (CB) is representative for the entire tumor, since CB is an important tool in breast cancer diagnosis. Moreover, we investigated whether performing CBs prior to the surgical excision could influence the GEP of the respective tumor. METHODS: We quantified the RNA expression of 60 relevant genes by quantitative real-time PCR in paired CBs and surgical specimens from 22 untreated primary breast cancer patients. Subsequently, expression data were compared with independent GEPs obtained from tumors of 317 patients without preceding CB. RESULTS: In 82% of the cases the GEP detected in the CB correlated very well with the corresponding profile in the surgical sample (r(s )≥ 0.95, p < 0.001). Gene-by-gene analysis revealed four genes significantly elevated in the surgical sample compared to the CB; these comprised genes mainly involved in inflammation and the wound repair process as well as in tumor invasion and metastasis. CONCLUSION: A GEP detected in a CB are representative for the entire tumor and is, therefore, of clinical relevance. The observed alterations of individual genes after performance of CB deserve attention since they might impact the clinical interpretation with respect to prognosis and therapy prediction of the GEP as detected in the surgical specimen following CB performance

Experimental Quantum Hamiltonian Learning

Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational physics. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies. Here we interface two different quantum systems through a classical channel - a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen-vacancy centre - and use the former to learn the latter's Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately $10^{-5}$. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model itself. We go on to implement an interactive version of the protocol and experimentally show its ability to characterise the operation of the quantum photonic device. This work demonstrates powerful new quantum-enhanced techniques for investigating foundational physical models and characterising quantum technologies

Holocene hydro-climatic variability in the Mediterranean: A synthetic multi-proxy reconstruction

Here we identify and analyze proxy data interpreted to reflect hydro-climatic variability over the last 10,000 years from the Mediterranean region to (1) outline millennial and multi-centennial-scale trends and (2) identify regional patterns of hydro-climatic variability. A total of 47 lake, cave, and marine records were transformed to z-scores to allow direct comparisons between sites, put on a common time scale, and binned into 200-year time slices. Six different regions were identified based on numerical and spatial analyzes of z-scores: S Iberia and Maghreb, N Iberia, Italy, the Balkans, Turkey, and the Levant, and the overall hydro-climate history of each region was reconstructed. N Iberia is largely decoupled from the five other regions throughout the Holocene. Wetter conditions occur in the five other regions between 8500 and 6100 yr BP. After 6000 yr BP, climate oscillated until around 3000 ± 300 yr BP, which seems to have been the overall driest period in the eastern Mediterranean and North Africa. In contrast, Italy and N Iberia seem to have remained wetter during this period. In addition, non-metric multidimensional scaling (nMDS) was applied to 18 long, continuous climate z-score records that span the majority of the Holocene. nMDS axes 1 and 2 illustrate the main trends in the z-score data. The first axis captures a long-term development of drier condition in the Mediterranean from 7900 to 3700 yr BP. Rapid shifts occur in nMDS axis 2 at 6700–6300 BP, 4500–4300 BP, and 3500–3300 BP indicating centennial-scale climate change. Our synthesis highlights a dominant south/east versus north/west Mediterranean hydro-climate dipole throughout the Holocene and therefore confirms that there was no single climate trajectory characterizing the whole Mediterranean basin during the last 10 millennia

Pollen-inferred regional vegetation patterns and demographic change in Southern Anatolia through the Holocene

Southern Anatolia is a highly significant area within the Mediterranean, particularly in terms of understanding how agriculture moved into Europe from neighbouring regions. This study uses pollen, palaeoclimate and archaeological evidence to investigate the relationships between demography and vegetation change, and to explore how the development of agriculture varied spatially. Data from 21 fossil pollen records have been transformed into forested, parkland and open vegetation types using cluster analysis. Patterns of change have been explored using non-metric multidimensional scaling (nMDS) and through analysis of indicator groups, such as an Anthropogenic Pollen Index, and Simpson’s Diversity. Settlement data, which indicate population densities, and summed radiocarbon dates for archaeological sites have been used as a proxy for demographic change. The pollen and archaeological records confirm that farming can be detected earlier in Anatolia in comparison with many other parts of the Mediterranean. Dynamics of change in grazing indicators and the OJCV (Olea, Juglans, Castanea and Vitis) index for cultivated trees appear to match cycles of population expansion and decline. Vegetation and land use change is also influenced by other factors, such as climate change. Investigating the early impacts of anthropogenic activities (e.g. woodcutting, animal herding, the use of fire and agriculture) is key to understanding how societies have modified the environment since the mid–late Holocene, despite the capacity of ecological systems to absorb recurrent disturbances. The results of this study suggest that shifting human population dynamics played an important role in shaping land cover in central and southern Anatolia

Loss of heterozygosity of TRIM3 in malignant gliomas

<p>Abstract</p> <p>Background</p> <p>Malignant gliomas are frequent primary brain tumors associated with poor prognosis and very limited response to conventional chemo- and radio-therapies. Besides sharing common growth features with other types of solid tumors, gliomas are highly invasive into adjacent brain tissue, which renders them particularly aggressive and their surgical resection inefficient. Therefore, insights into glioma formation are of fundamental interest in order to provide novel molecular targets for diagnostic purposes and potential anti-cancer drugs. Human <it>Tripartite motif protein 3 </it>(<it>TRIM3</it>) encodes a structural homolog of <it>Drosophila brain tumor </it>(<it>brat</it>) implicated in progenitor cell proliferation control and cancer stem cell suppression. <it>TRIM3 </it>is located within the loss of allelic heterozygosity (LOH) hotspot of chromosome segment 11p15.5, indicating a potential role in tumor suppression. ...</p> <p>Methods</p> <p>Here we analyze 70 primary human gliomas of all types and grades and report somatic deletion mapping as well as single nucleotide polymorphism analysis together with quantitative real-time PCR of chromosome segment 11p15.5.</p> <p>Results</p> <p>Our analysis identifies LOH in 17 cases (24%) of primary human glioma which defines a common 130 kb-wide interval within the <it>TRIM3 </it>locus as a minimal area of loss. We further detect altered genomic dosage of <it>TRIM3 </it>in two glioma cases with LOH at 11p15.5, indicating homozygous deletions of <it>TRIM3</it>.</p> <p>Conclusion</p> <p>Loss of heterozygosity of chromosome segment 11p15.5 in malignant gliomas suggests <it>TRIM3 </it>as a candidate brain tumor suppressor gene.</p

PDK1 and HR46 Gene Homologs Tie Social Behavior to Ovary Signals

The genetic basis of division of labor in social insects is a central question in evolutionary and behavioral biology. The honey bee is a model for studying evolutionary behavioral genetics because of its well characterized age-correlated division of labor. After an initial period of within-nest tasks, 2–3 week-old worker bees begin foraging outside the nest. Individuals often specialize by biasing their foraging efforts toward collecting pollen or nectar. Efforts to explain the origins of foraging specialization suggest that division of labor between nectar and pollen foraging specialists is influenced by genes with effects on reproductive physiology. Quantitative trait loci (QTL) mapping of foraging behavior also reveals candidate genes for reproductive traits. Here, we address the linkage of reproductive anatomy to behavior, using backcross QTL analysis, behavioral and anatomical phenotyping, candidate gene expression studies, and backcross confirmation of gene-to-anatomical trait associations. Our data show for the first time that the activity of two positional candidate genes for behavior, PDK1 and HR46, have direct genetic relationships to ovary size, a central reproductive trait that correlates with the nectar and pollen foraging bias of workers. These findings implicate two genes that were not known previously to influence complex social behavior. Also, they outline how selection may have acted on gene networks that affect reproductive resource allocation and behavior to facilitate the evolution of social foraging in honey bees

GSK3β Regulates Differentiation and Growth Arrest in Glioblastoma

Cancers are driven by a population of cells with the stem cell properties of self-renewal and unlimited growth. As a subpopulation within the tumor mass, these cells are believed to constitute a tumor cell reservoir. Pathways controlling the renewal of normal stem cells are deregulated in cancer. The polycomb group gene Bmi1, which is required for neural stem cell self-renewal and also controls anti-oxidant defense in neurons, is upregulated in several cancers, including medulloblastoma. We have found that Bmi1 is consistently and highly expressed in GBM. Downregulation of Bmi1 by shRNAs induced a differentiation phenotype and reduced expression of the stem cell markers Sox2 and Nestin. Interestingly, expression of glycogen synthase kinase 3 beta (GSK3β), which was found to be consistently expressed in primary GBM, also declined. This suggests a functional link between Bmi1 and GSK3β. Interference with GSK3β activity by siRNA, the specific inhibitor SB216763, or lithium chloride (LiCl) induced tumor cell differentiation. In addition, tumor cell apoptosis was enhanced, the formation of neurospheres was impaired, and clonogenicity reduced in a dose-dependent manner. GBM cell lines consist mainly of CD133-negative (CD133-) cells. Interestingly, ex vivo cells from primary tumor biopsies allowed the identification of a CD133- subpopulation of cells that express stem cell markers and are depleted by inactivation of GSK3β. Drugs that inhibit GSK3, including the psychiatric drug LiCl, may deplete the GBM stem cell reservoir independently of CD133 status

Evaluating model outputs using integrated global speleothem records of climate change since the last glacial

Although quantitative isotope data from speleothems has been used to evaluate isotope-enabled model simulations, currently no consensus exists regarding the most appropriate methodology through which to achieve this. A number of modelling groups will be running isotope-enabled palaeoclimate simulations in the framework of the Coupled Model Intercomparison Project Phase 6, so it is timely to evaluate different approaches to using the speleothem data for data–model comparisons. Here, we illustrate this using 456 globally distributed speleothem δ18O records from an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5-wiso isotope-enabled atmospheric circulation model. We show that the SISAL records reproduce the first-order spatial patterns of isotopic variability in the modern day, strongly supporting the application of this dataset for evaluating model-derived isotope variability into the past. However, the discontinuous nature of many speleothem records complicates the process of procuring large numbers of records if data–model comparisons are made using the traditional approach of comparing anomalies between a control period and a given palaeoclimate experiment. To circumvent this issue, we illustrate techniques through which the absolute isotope values during any time period could be used for model evaluation. Specifically, we show that speleothem isotope records allow an assessment of a model's ability to simulate spatial isotopic trends. Our analyses provide a protocol for using speleothem isotope data for model evaluation, including screening the observations to take into account the impact of speleothem mineralogy on δ18O values, the optimum period for the modern observational baseline and the selection of an appropriate time window for creating means of the isotope data for palaeo-time-slices