Undecidability of the Spectral Gap in One Dimension

The spectral gap problem—determining whether the energy spectrum of a system has an energy gap above ground state, or if there is a continuous range of low-energy excitations—pervades quantum many-body physics. Recently, this important problem was shown to be undecidable for quantum-spin systems in two (or more) spatial dimensions: There exists no algorithm that determines in general whether a system is gapped or gapless, a result which has many unexpected consequences for the physics of such systems. However, there are many indications that one-dimensional spin systems are simpler than their higher-dimensional counterparts: For example, they cannot have thermal phase transitions or topological order, and there exist highly effective numerical algorithms such as the density matrix renormalization group—and even provably polynomial-time ones—for gapped 1D systems, exploiting the fact that such systems obey an entropy area law. Furthermore, the spectral gap undecidability construction crucially relied on aperiodic tilings, which are not possible in 1D. So does the spectral gap problem become decidable in 1D? In this paper, we prove this is not the case by constructing a family of 1D spin chains with translationally invariant nearest-neighbor interactions for which no algorithm can determine the presence of a spectral gap. This not only proves that the spectral gap of 1D systems is just as intractable as in higher dimensions, but it also predicts the existence of qualitatively new types of complex physics in 1D spin chains. In particular, it implies there are 1D systems with a constant spectral gap and nondegenerate classical ground state for all systems sizes up to an uncomputably large size, whereupon they switch to a gapless behavior with dense spectrum

Probing Patchy Reionization with the Void Probability Function of Lyman-α\alpha Emitters

We probe what constraints for the global ionized hydrogen fraction the Void Probability Function (VPF) clustering can give for the Lyman-Alpha Galaxies in the Epoch of Reionization (LAGER) narrowband survey as a function of area. Neutral hydrogen acts like a fog for Lyman-alpha emission, and measuring the drop in the luminosity function of Lyman-$\alpha$ emitters (LAEs) has been used to constrain the ionization fraction in narrowband surveys. However, the clustering of LAEs is independent from the luminosity function's inherent evolution, and can offer additional constraints for reionization under different models. The VPF measures how likely a given circle is to be empty. It is a volume-averaged clustering statistic that traces the behavior of higher order correlations, and its simplicity offers helpful frameworks for planning surveys. Using the \citet{Jensen2014} simulations of LAEs within various amount of ionized intergalactic medium, we predict the behavior of the VPF in one (301x150.5x30 Mpc$^3$), four (5.44$\times 10^6$ Mpc$^3$), or eight (1.1$\times 10^7$ Mpc$^3$) fields of LAGER imaging. We examine the VPF at 5 and 13 arcminutes, corresponding to the minimum scale implied by the LAE density and the separation of the 2D VPF from random, and the maximum scale from the 8-field 15.5 deg$^2$ LAGER area. We find that even a single DECam field of LAGER (2-3 deg$^2$) could discriminate between mostly neutral vs. ionized. Additionally, we find four fields allows the distinction between 30, 50, and 95 percent ionized; and that eight fields could even distinguish between 30, 50, 73, and 95 percent ionized.Comment: 14 pages, 5 figure

Algal nutraceuticals: a perspective on metabolic diversity, current food applications, and prospects in the field of metabolomics

The current consumers’ demand for food naturalness is urging the search for new functional foods of natural origin with enhanced health-promoting properties. In this sense, algae constitute an underexplored biological source of nutraceuticals that can be used to fortify food products. Both marine macroalgae (or seaweeds) and microalgae exhibit a myriad of chemical constituents with associated features as a result of their primary and secondary metabolism. Thus, primary metabolites, especially polysaccharides and phycobiliproteins, present interesting properties to improve the rheological and nutritional properties of food matrices, whereas secondary metabolites, such as polyphenols and xanthophylls, may provide interesting bioactivities, including antioxidant or cytotoxic effects. Due to the interest in algae as a source of nutraceuticals by the food and related industries, novel strategies should be undertaken to add value to their derived functional components. As a result, metabolomics is considered a high throughput technology to get insight into the full metabolic profile of biological samples, and it opens a wide perspective in the study of algae metabolism, whose knowledge is still little explored. This review focuses on algae metabolism and its applications in the food industry, paying attention to the promising metabolomic approaches to be developed aiming at the functional characterization of these organisms.The research leading to these results was supported by the European Union through the “NextGenerationEU” program supporting the “Margarita Salas” grant awarded to P. Garcia-Perez, by Xunta de Galicia for supporting the program EXCELENCIA-ED431F 2020/12, the postdoctoral grant of L. Cassani (ED481B-2021/152), and the pre-doctoral grant of P. Garcia-Oliveira (ED481A-2019/295) and by MICINN supporting the Ram´on y Cajal grant for M.A. Prieto (RYC-2017-22891) and J. Xiao (RYC-2020-030365-I). Authors are grateful to Bio Based Industries Joint Undertaking (JU) under grant agreement No 888003 UP4HEALTH Project (H2020-BBI-JTI-2019). The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Bio Based Industries Consortium. This work has also received funding from the Argentinean Agency for the Scientific and Technological Promotion (ANPCyT, Argentina) under the project PICT (2020)/1602.info:eu-repo/semantics/publishedVersio

Size-driven quantum phase transitions

Can the properties of the thermodynamic limit of a many-body quantum system be extrapolated by analyzing a sequence of finite-size cases? We present models for which such an approach gives completely misleading results: translationally invariant, local Hamiltonians on a square lattice with open boundary conditions and constant spectral gap, which have a classical product ground state for all system sizes smaller than a particular threshold size, but a ground state with topological degeneracy for all system sizes larger than this threshold. Starting from a minimal case with spins of dimension 6 and threshold lattice size 15×1515×15, we show that the latter grows faster than any computable function with increasing local spin dimension. The resulting effect may be viewed as a unique type of quantum phase transition that is driven by the size of the system rather than by an external field or coupling strength. We prove that the construction is thermally robust, showing that these effects are in principle accessible to experimental observation

First measurement of the K−n →Λπ−non-resonant transition amplitude below threshold

We present the analysis of K−absorption processes on He4 leading to Λπ−final states, measured with the KLOE spectrometer at the DAΦNE e+e−collider and extract, for the first time, the modulus of the non-resonant K−n →Λπ−direct production amplitude about 33 MeV below the K‾N threshold. This analysis also allows to disentangle the K−nuclear absorption at-rest from the in-flight capture, for K−momenta of about 120 MeV. The data are interpreted with the help of a phenomenological model, and the modulus of the non-resonant K−n →Λπ−amplitude for K−absorption at-rest is found to be |AK−n→Λπ−|=(0.334±0.018stat−0.058+0.034syst)fm

How, where and when is SPINK3 bound and removed from mouse sperm?

Sperm capacitation in mammals is a fundamental requirement to acquire their fertilizing capacity. Little is known about the action mechanism of the molecules that prevent capacitation from occurring prematurely. These molecules are known as decapacitation factors (DFs) and they must be removed from the sperm surface for capacitation to occur successfully. Serine protease inhibitor Kazal type 3 (SPINK3) has been proposed as one of these DFs. Here, we evaluate how this protein binds to mouse sperm and its removal kinetics. We describe that SPINK3 is capable of binding to the membrane of mature epididymal sperm through protein-lipid interactions, specifically to lipid rafts subcellular fraction. Moreover, cholera toxin subunit b (CTB) avoids SPINK3 binding. We observe that SPINK3 is removed from the sperm under in vitro capacitating conditions and by the uterine fluid from estrus females. Our ex vivo studies show the removal kinetics of this protein within the female tract, losing SPINK3 formerly from the apical region of the sperm in the uterus and later from the flagellar region within the oviduct. The presence of acrosome-reacted sperm in the female duct concurs with the absence of SPINK3 over its surface.Fil: Nicolli, Anabella Rita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Alonso, Carlos A. I.. McGill University; CanadáFil: Otamendi, Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Cerletti, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Poetsch, Ansgar. Ruhr Universität Bochum; AlemaniaFil: Sharma, Vikram. University of Plymouth; Reino UnidoFil: Zalazar, Lucia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Perez Martinez, Silvina Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Cesari, Andreina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; Argentin

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and $\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.Comment: 40 pages, 22 figures (11 made of subfigures

Untargeted metabolomics and in vitro functional analysis unravel the intraspecific bioactive potential of flowers from underexplored Camellia japonica cultivars facing their industrial application

The Camellia genus comprises a vast array of underexplored medicinal plants that merit a systematic valorization to exploit their potential as natural sources of phytochemicals with associated health-promoting properties. In this work, flower extracts from eight poorly characterized Camellia japonica L. cultivars were subjected to polyphenol profiling through untargeted metabolomics combined with in vitro functional analysis. Anthocyanins, mostly represented by cyanidin 3-O-glycosides, flavones, and flavonols, were found as the major constituents of C. japonica flowers, together with hydroxycinnamic acids, tyrosols, alkylphenols, and stilbenes, which were detected for the first time in this species. The application of multivariate statistics revealed a flower colordependent fingerprint of C. japonica cultivars, featuring anthocyanins and other flavonoids as metabolite markers associated with color-flowered cultivars with respect to white-flowered ones. The accumulation of anthocyanins, especially reported in ‘Eugenia de Montijo’ flowers, was highly correlated with the cytotoxic and anti-inflammatory properties of the derived extracts, including AGS, Caco-2, and MCF7 cancer cell lines. Moreover, the flavones accumulation reported in ‘Carolyn Tuttle’ extracts was also associated with high rates of free-radical scavenging activity, as well as a potent cytotoxicity against the Caco-2 cell line. In general, C. japonica anthocyanin-enriched flower extracts were revealed as promising candidates for the industrial production of polyphenols with associated biological activities of high interest for critical sectors in the food, pharmaceutical, and cosmetic industries.The research leading to these results was supported by MICINN supporting the Ramón y Cajal grant for M.A.-P. (RYC-2017–22891) and the Juan de la Cierva Formación grant for T.-O. (FJC2019–042549-I). The authors acknowledge Xunta de Galicia for funding the post-doctoral grant of L. C. (ED481B-2021/152) and the program EXCELENCIAED431F 2020/12, which supported the work by F.C. The authors are also grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2020), and national funding by FCT, P.I., through the institutional scientific employment program contract for L.-B. and R. C.-C. The work by P.G.-P. was financed by the Spanish Ministry of Universities under the application 33.50.460A.752 and by the European Union NextGenerationEU/PRTR through a Margarita Salas contract by the Universidade de Vigo.info:eu-repo/semantics/publishedVersio