54,637 research outputs found

    Efficient inference in the transverse field Ising model

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    In this paper we introduce an approximate method to solve the quantum cavity equations for transverse field Ising models. The method relies on a projective approximation of the exact cavity distributions of imaginary time trajectories (paths). A key feature, novel in the context of similar algorithms, is the explicit separation of the classical and quantum parts of the distributions. Numerical simulations show accurate results in comparison with the sampled solution of the cavity equations, the exact diagonalization of the Hamiltonian (when possible) and other approximate inference methods in the literature. The computational complexity of this new algorithm scales linearly with the connectivity of the underlying lattice, enabling the study of highly connected networks, as the ones often encountered in quantum machine learning problems

    Changes in PRC1 activity during interphase modulate lineage transition in pluripotent cells

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    The potential of pluripotent cells to respond to developmental cues and trigger cell differentiation is enhanced during the G1 phase of the cell cycle, but the molecular mechanisms involved are poorly understood. Variations in polycomb activity during interphase progression have been hypothesized to regulate the cell-cycle-phase-dependent transcriptional activation of differentiation genes during lineage transition in pluripotent cells. Here, we show that recruitment of Polycomb Repressive Complex 1 (PRC1) and associated molecular functions, ubiquitination of H2AK119 and three-dimensional chromatin interactions, are enhanced during S and G2 phases compared to the G1 phase. In agreement with the accumulation of PRC1 at target promoters upon G1 phase exit, cells in S and G2 phases show firmer transcriptional repression of developmental regulator genes that is drastically perturbed upon genetic ablation of the PRC1 catalytic subunit RING1B. Importantly, depletion of RING1B during retinoic acid stimulation interferes with the preference of mouse embryonic stem cells (mESCs) to induce the transcriptional activation of differentiation genes in G1 phase. We propose that incremental enrolment of polycomb repressive activity during interphase progression reduces the tendency of cells to respond to developmental cues during S and G2 phases, facilitating activation of cell differentiation in the G1 phase of the pluripotent cell cycle

    Utilization of silica-enriched filter cake industry by-products as partial ordinary portland cement replacement

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    In recent years, industrial byproducts have been converted into useful and valuable commercial items. Reusing these byproducts plays a crucial role to ensure the circular economy and thereby safeguard the environmental impacts. In Ethiopia, the Aluminate Sulphate chemical factory disposes of filter-cake waste materials in landfills that have high silica content. The factory is using pure kaolin and other raw materials for the production of Aluminum Sulphate and Sulphuric Acid by burning at high temperatures. By-products materials were collected from the factory and then calcined (post-treated) at 600 °C for 2h in a muffle furnace. From Atomic Absorption Spectrometry measurement result, it is confirmed that the post-treated (at 600 °C/2h) silica-enriched filter-cake waste materials have a similar composition to Metakaolin (MK). Post-treated filter cake (named MK) became more amorphous having high reactive silica with very low impurities as it was calcined and quenched rapidly. In this study, the properties of blended Ordinary Portland Cement (OPC)-mortar samples were investigated with the addition of heat-treated filter cake waste materials (0%–20%) as a partial OPC replacement. X-ray diffraction, Fourier Transform-Infrared Spectroscopy, Differential Thermal Analysis, Scanning Electron Microscope, and Atomic Absorption Spectrometry were used to investigate the properties of mortar samples that contain post-treated filter-cake (MK) materials and OPC-cement. The flexural and compressive strengths of 10% MK + 90% OPC-mortar samples were enhanced at early curing ages, 7 & 28 days. Moreover, the flexural and compressive strengths of OPC mortars with 15% MK have been improved at 28 days of curing age. However, 20% MK + 80% OPC blended mortars have not shown any improvement in mechanical properties. Setting time, soundness, water absorption, and apparent porosity of cement pastes with the addition of post-treated filter cake (MK) are also analyzed

    Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions

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    Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms. Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 μg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis. Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1 1.1 Welcome to the anthropocene .......................................................................... 1 1.2 From cellular stress responses to ecosystem resilience ................................... 3 1.2.1 The individual pursuit for homeostasis ....................................................... 3 1.2.2 Stability from diversity ................................................................................. 5 1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical pollution? ................................................................................................................. 6 1.4 Functional ecotoxicological assessment of microbial communities ................... 9 1.5 Molecular tools – the key to a mechanistic understanding of stressor effects from a functional perspective in microbial communities? ...................................... 12 2. Aims and Hypothesis ......................................................................................... 14 2.1 Research question .......................................................................................... 14 2.2 Hypothesis and outline .................................................................................... 15 2.3 Experimental approach & concept .................................................................. 16 2.3.1 Aquatic freshwater biofilms as model community ..................................... 16 2.3.2 Diuron as model herbicide ........................................................................ 17 2.3.3 Experimental design ................................................................................. 18 3. Structural and physiological changes in microbial communities after chronic exposure - PICT and altered functional capacity ................................................. 21 3.1 Introduction ..................................................................................................... 21 3.2 Methods .......................................................................................................... 23 3.2.1 Biofilm cultivation ...................................................................................... 23 3.2.2 Dry weight and autotrophic index ............................................................. 23 3.2.4 Pigment analysis of periphyton ................................................................. 23 3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24 3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24 3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26 3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27 3.2.5 Community oxygen metabolism measurements ....................................... 28 3.3 Results and discussion ................................................................................... 29 3.3.1 Comparison of the structural community parameters ............................... 29 3.3.2 Photosynthetic activity and primary production of the communities after selection phase ................................................................................................. 33 3.3.3 Acquisition of photosynthetic tolerance .................................................... 34 3.3.4 Primary production at exposure conditions ............................................... 36 3.3.5 Tolerance detection in primary production ................................................ 37 3.4 Summary and Conclusion ........................................................................... 40 4. Community gene expression analysis by meta-transcriptomics ................... 41 4.1 Introduction to meta-transcriptomics ............................................................... 41 4.2. Methods ......................................................................................................... 43 4.2.1 Sampling and RNA extraction................................................................... 43 4.2.2 RNA sequencing analysis ......................................................................... 44 4.2.3 Data assembly and processing................................................................. 45 4.2.4 Prioritization of contigs and annotation ..................................................... 47 4.2.5 Sensitivity analysis of biological processes .............................................. 48 4.3 Results and discussion ................................................................................... 48 4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49 4.3.2 Insights into community stress response mechanisms using trend analysis (DRomic’s) ......................................................................................................... 51 4.3.3 Response pattern in the isoform PS genes .............................................. 63 4.5 Summary and conclusion ................................................................................ 65 5. Community metabolome analysis ..................................................................... 66 5.1 Introduction to community metabolomics ........................................................ 66 5.2 Methods .......................................................................................................... 68 5.2.1 Sampling, metabolite extraction and derivatisation................................... 68 5.2.2 GC-TOF-MS analysis ............................................................................... 69 5.2.3 Data processing and statistical analysis ................................................... 69 5.3 Results and discussion ................................................................................... 70 5.3.1 Characterization of the metabolic fingerprints .......................................... 70 5.3.2 Difference in the metabolic fingerprints .................................................... 71 5.3.3 Differential metabolic responses of the communities to short-term exposure of diuron ............................................................................................................ 73 5.4 Summary and conclusion ................................................................................ 78 6. Synthesis ............................................................................................................. 79 6.1 Approaches and challenges for linking molecular data to functional measurements ...................................................................................................... 79 6.2 Methods .......................................................................................................... 83 6.2.1 Summary on the data ............................................................................... 83 6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83 6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83 6.3 Results and discussion ................................................................................... 85 6.3.1 Results of aggregation techniques ........................................................... 85 6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86 6.3.3 Mechanistic view of the molecular stress responses based on KEGG functions ............................................................................................................ 89 6.4 Consolidation of the results – holistic interpretation and discussion ............... 93 6.4.1 Adaptation to chronic diuron exposure - from molecular changes to community effects.............................................................................................. 93 6.4.2 Assessment of the ecological costs of Pollution-induced community tolerance based on primary production ............................................................. 94 6.5 Outlook ............................................................................................................ 9

    Hybrid time-dependent Ginzburg-Landau simulations of block copolymer nanocomposites: nanoparticle anisotropy

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    Block copolymer melts are perfect candidates to template the position of colloidal nanoparticles in the nanoscale, on top of their well-known suitability for lithography applications. This is due to their ability to self-assemble into periodic ordered structures, in which nanoparticles can segregate depending on the polymer-particle interactions, size and shape. The resulting coassembled structure can be highly ordered as a combination of both the polymeric and colloidal properties. The time-dependent Ginzburg-Landau model for the block copolymer was combined with Brownian dynamics for nanoparticles, resulting in an efficient mesoscopic model to study the complex behaviour of block copolymer nanocomposites. This review covers recent developments of the time-dependent Ginzburg-Landau/Brownian dynamics scheme. This includes efforts to parallelise the numerical scheme and applications of the model. The validity of the model is studied by comparing simulation and experimental results for isotropic nanoparticles. Extensions to simulate nonspherical and inhomogeneous nanoparticles are discussed and simulation results are discussed. The time-dependent Ginzburg-Landau/Brownian dynamics scheme is shown to be a flexible method which can account for the relatively large system sizes required to study block copolymer nanocomposite systems, while being easily extensible to simulate nonspherical nanoparticles

    Four Lectures on the Random Field Ising Model, Parisi-Sourlas Supersymmetry, and Dimensional Reduction

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    Numerical evidence suggests that the Random Field Ising Model loses Parisi-Sourlas SUSY and the dimensional reduction property somewhere between 4 and 5 dimensions, while a related model of branched polymers retains these features in any dd. These notes give a leisurely introduction to a recent theory, developed jointly with A. Kaviraj and E. Trevisani, which aims to explain these facts. Based on the lectures given in Cortona and at the IHES in 2022.Comment: 55 pages, 11 figures; v2 - minor changes, mentioned forthcoming work by Fytas et a

    Monte Carlo analysis of the contributions of long-lived positronium to the spectra of positron-impact-induced secondary electrons measured using an annihilation-gamma-triggered time-of-flight spectrometer

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    Magnetic bottle Time-of-Flight (ToF) spectrometers can measure the energy spectra of all electrons emitted into a 2π\pi sr solid angle simultaneously, greatly reducing data collection time. When the detection of the annihilation gamma (γ\gamma) and the detection of the electron (e) are used as timing signals for ToF spectrometers, the e-γ\gamma time difference spectra (e-γ\gamma TDS) are reflective of the positron-induced electron energy distributions provided the times between the impact of the positrons and the emission of the annihilation gammas are short compared to the flight times of the electrons. This is typically the case since positrons have short lifetime in solids (\sim 100 - 500 ps) compared to the flight times of the secondary electrons (10210^2 ns to 10310^3 ns). However, if the positron leaves the surface as a positronium atom (a bound electron-positron state), the annihilation gamma photons can be appreciably delayed due to the longer ortho-positronium (o-Ps) lifetime. This can result in an e-γ\gamma TDS having an exponential tail with a decay constant related to the o-Ps lifetime. Here, we present an analysis of the e-γ\gamma TDS using a Monte Carlo model which estimates the spectral contributions resulting from o-Ps annihilations. By removing the contributions from the delayed gamma signal, the energy spectrum of Positron Impact-Induced Secondary electrons (PIISE) can be isolated. Furthermore, our analysis allows an estimation of the intensity of the exponential tail in the e-γ\gamma TDS providing a method to measure the fraction of positrons that form Ps at solid surfaces without relying on assumed 100% Ps emitting surfaces for calibration

    Anti-schistosomal immunity to core xylose/fucose in N-glycans

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    Schistosomiasis is a globally prevalent, debilitating disease that is poorly controlled by chemotherapy and for which no vaccine exists. While partial resistance in people may develop over time with repeated infections and treatments, some animals, including the brown rat (Rattus norvegicus), are only semi-permissive and have natural protection. To understand the basis of this protection, we explored the nature of the immune response in the brown rat to infection by Schistosoma mansoni. Infection leads to production of IgG to Infection leads to production of IgG to parasite glycoproteins parasite glycoproteins with complex-type N-glycans that contain a non-mammalian-type modification by core α2-Xylose and core α3-Fucose (core Xyl/Fuc). These epitopes are expressed on the surfaces of schistosomula and adult worms. Importantly, IgG to these epitopes can kill schistosomula by a complement-dependent process in vitro. Additionally, sera from both infected rhesus monkey and infected brown rat were capable of killing schistosomula in a manner inhibited by glycopeptides containing core Xyl/Fuc. These results demonstrate that protective antibodies to schistosome infections in brown rats and rhesus monkeys include IgG responses to the core Xyl/Fuc epitopes in surface-expressed N-glycans, and raise the potential of novel glyco-based vaccines that might be developed to combat this disease

    Random Young towers and quenched decay of correlations for predominantly expanding multimodal circle maps

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    In this paper, we study the random dynamical system fωnf_\omega^n generated by a family of maps {fω0:S1S1}ω[ε,ε],\{f_{\omega_0}: \mathbb S^1 \to \mathbb S^1\}_{\omega \in [-\varepsilon,\varepsilon]}, $f_{\omega_0}(x) = \alpha \xi (x+\omega_0) +a\ (\mathrm{mod }\ 1),where where \xi: \mathbb S^1 \to \mathbb Risanondegeneratedmap, is a non-degenerated map, a\in \mathbb S^1,, \alpha,\varepsilon>0.Fixingaconstant. Fixing a constant c\in (0,1),weshowthatfor, we show that for \alphasufficientlylargeand sufficiently large and \varepsilon > \alpha^{-1+c},therandomdynamicalsystem the random dynamical system f_\omega^n$ presents a random Young tower structure and quenched decay of correlations.Comment: 38 pages, 0 figure
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