Evolution of the magnetic properties and magnetic structures along the RmMIn3m+2 (R = Ce, Nd, Gd, Tb; M = Rh, Ir; and m=1,2) series of intermetallic compounds

We discuss the evolution of the magnetic properties and magnetic structures along the series of intermetallic compounds RmMIn3m+2 (R=Ce, Nd, Gd, Tb; M=Rh, Ir; and m=1,2). The m=1,2 are, respectively, the single layer and bilayer tetragonal derivatives of their cubic RIn3 relatives. Using a mean field model including an isotropic first-neighbors Ruderman-Kittel-Kasuya-Yoshida interaction (K) and the tetragonal crystalline electrical field (CEF), we demonstrated that, for realistic values of K and CEF parameters, one can qualitatively describe the direction of the ordered moments and the behavior of the ordering temperature for these series. The particular case, where the rare-earth ordered moments lie in the ab plane or are tilted from the c axis and T-N can be reduced by tuning the CEF parameters, revealed an interesting kind of frustration that may be relevant to the physical properties of complex classes of materials such as the RmMIn3m+2 (M=Rh, Ir, and Co; m=1,2) heavy-fermion superconductors. (C) 2006 American Institute of Physics.99

The efficacy of chemotherapy is limited by intratumoral senescent cells expressing PD-L2

Chemotherapy often generates intratumoral senescent cancer cells that strongly modify the tumor microenvironment, favoring immunosuppression and tumor growth. We discovered, through an unbiased proteomics screen, that the immune checkpoint inhibitor programmed cell death 1 ligand 2 (PD-L2) is highly upregulated upon induction of senescence in different types of cancer cells. PD-L2 is not required for cells to undergo senescence, but it is critical for senescent cells to evade the immune system and persist intratumorally. Indeed, after chemotherapy, PD-L2-deficient senescent cancer cells are rapidly eliminated and tumors do not produce the senescence-associated chemokines CXCL1 and CXCL2. Accordingly, PD-L2-deficient pancreatic tumors fail to recruit myeloid-derived suppressor cells and undergo regression driven by CD8 T cells after chemotherapy. Finally, antibody-mediated blockade of PD-L2 strongly synergizes with chemotherapy causing remission of mammary tumors in mice. The combination of chemotherapy with anti-PD-L2 provides a therapeutic strategy that exploits vulnerabilities arising from therapy-induced senescence. © 2024, The Author(s)

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of $\sigma_{\Delta z} = 0.007$, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA

Developing country consumers’ acceptance of biofortified foods: a synthesis

The success of biofortified staple crops depends on whether they are accepted and consumed by target populations. In the past 8 years, several studies were undertaken to understand consumers’ acceptance of foods made with biofortified staple crops. Consumer acceptance is measured in terms of their sensory evaluation and economic valuation of biofortified varieties vis-à-vis conventional ones. These studies apply expert sensory panel and hedonic trait analyses methods adopted from food sciences literature, as well as various preference elicitation methods (including experimental auctions, revealed choice experiments, and stated choice experiments) adopted from experimental economics literature. These studies also test the impact of various levers on consumers’ evaluation and valuation for biofortified foods. These levers include (i) nutrition information and the media through which such information is conveyed; (ii) the length and content of nutrition information; (iii) different branding options; (iv) the nature (national or international) of the branding/certification agency that is endorsing the biofortified staple food; and (v) the nature (national or international) of the agency that is delivering the biofortified staple food. This paper brings together evidence on consumer acceptance of biofortified crops on 5 crops across 7 countries in Africa, Asia and Latin America. The results of these studies are expected to aid in the development of biofortified crops that consumers like, as well as in the development of appropriate marketing and consumer awareness or information campaigns to encourage the switch in consumption from traditional staples to biofortified ones

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

Minimization of Biosynthetic Costs in Adaptive Gene Expression Responses of Yeast to Environmental Changes

Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses

A DECam Search for Explosive Optical Transients Associated with IceCube Neutrino Alerts

In this work, we investigate the likelihood of association between real-time, neutrino alerts with teraelectronvolt to petaelectronvolt energy from IceCube and optical counterparts in the form of core-collapse supernovae (CC SNe). The optical follow-up of IceCube alerts requires two main instrumental capabilities: (1) deep imaging, since 73% of neutrinos would come from CC SNe at redshifts z > 0.3, and (2) a large field of view (FoV), since typical IceCube muon neutrino pointing accuracy is on the order of ~1 deg. With Blanco/DECam (gri to 24th magnitude and 2.2 deg diameter FoV), we performed a triggered optical follow-up observation of two IceCube alerts, IC170922A and IC171106A, on six nights during the three weeks following each alert. For the IC170922A (IC171106A) follow-up observations, we expect that 12.1% (9.5%) of coincident CC SNe at z lesssim 0.3 are detectable, and that, on average, 0.23 (0.07) unassociated SNe in the neutrino 90% containment regions also pass our selection criteria. We find two candidate CC SNe that are temporally coincident with the neutrino alerts in the FoV, but none in the 90% containment regions, a result that is statistically consistent with expected rates of background CC SNe for these observations. If CC SNe are the dominant source of teraelectronvolt to petaelectronvolt neutrinos, we would expect an excess of coincident CC SNe to be detectable at the 3σ confidence level using DECam observations similar to those of this work for ~60 (~200) neutrino alerts with (without) redshift information for all candidates

Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. We present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum C ℓ and the redshift distribution n ( z ) of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of C ℓ and n ( z ) curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of Ω m , σ 8 , and intrinsic alignment amplitude A IA , defined as S 8 D IA = σ 8 ( Ω m / 0.3 ) 0.5 D IA , where D IA = 1 − 0.11 ( A IA − 1 ) . We find S 8 D IA = 0.89 5 + 0.054 − 0.039 , where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys