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The continuous internal bremsstrahlung spectrum accompanying a beta and neutrino from the 14 C isotope was measured by magnetic deflection method in the region of 10–150 keV using a 7.62 cm × 7.62 cm NaI(Tl) scintillation spectrometer. The NaI(Tl) detector was calibrated by the standard radioactive sources. The detector response functions are generated for single-energy gamma photons with the help of experimental detector parameters and the Monte Carlo method. The experimental set-up and the geometry were adjusted carefully to minimise the external bremsstrahlung effect and the undesirable gamma counts from the surroundings in the detector. The raw experimental data were unfolded from the detector response effects by applying the singular value decomposition method. The experimental spectrum was compared with both theoretical and Monte Carlo calculation spectra. [ABSTRACT FROM AUTHOR]

The red macroalgae Porphyra, commonly known as Nori, is widely used as food around the world due to its high nutrient content, including the significant abundance of colored phycobiliproteins (PBPs). Among these, R‐phycocyanin (R‐PC) stands out for its vibrant purple color and numerous bioactive properties, making it a valuable protein for the food industry. However, R‐PC's limited thermal stability necessitates alternative processing methods to preserve its color and bioactive properties. Our study aimed to investigate the in‐situ stability of oligomeric R‐PC under high pressure (HP) conditions (up to 4000 bar) using a combination of absorption, fluorescence, and small‐angle X‐ray scattering (SAXS) techniques. The unfolding of R‐PC is a multiphase process. Initially, low pressure induces conformational changes in the R‐PC oligomeric form (trimers). As pressure increases above 1600 bar, these trimers dissociate into monomers, and at pressures above 3000 bar, the subunits begin to unfold. When returned to atmospheric pressure, R‐PC partially refolds, retaining 50% of its original color absorbance. In contrast, heat treatment causes irreversible and detrimental effects on R‐PC color, highlighting the advantages of HP treatment in preserving both the color and bioactive properties of R‐PC compared to heat treatment. [ABSTRACT FROM AUTHOR]

The distortions of measured beta spectra are addressed by means of unfolding algorithms. Two different approaches, the Maximum-Likelihood ExpectationMaximization and the Tikhonov regularization, are tested on various simulated spectra, for which the initial spectrum to retrieve is known, and on a 99Tc spectrum measured with our dedicated setup. Statistical uncertainties of distorted measured spectra are propagated by determining the covariance matrices. Both algorithms provide satisfactory results but Tikhonov performs overall better for most of the studied radionuclides. Highlight is made on the necessity to employ at least two independent methods to ensure the accuracy of the unfolded spectra and to estimate the internal bias of each algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the upscaling of an elliptic problem with a highly oscillating quasilinear matrix coefficient, a quasilinear term, and a semilinear term in domains periodically perforated with holes of critical size. A Signorini boundary condition is imposed on the boundary of the holes, while a Dirichlet boundary condition is prescribed on the exterior boundary. Using the periodic unfolding method, we obtain an obstacle problem with a nonnegativity spreading effect. [ABSTRACT FROM AUTHOR]

In 2010, Butler introduced the unfolding operation on a bipartite graph to produce two bipartite graphs, which are cospectral for the adjacency and the normalized Laplacian matrices. In this article, we describe how the idea of unfolding a bipartite graph with respect to another bipartite graph can be extended to nonbipartite graphs. In particular, we describe how unfoldings involving reflexive bipartite, semi-reflexive bipartite, and multipartite graphs are used to obtain cospectral nonisomorphic graphs for the adjacency matrix. [ABSTRACT FROM AUTHOR]

We present a formalism for Petri nets based on polynomial-style finite-set configurations and etale maps. The formalism supports both a geometric semantics in the style of Goltz and Reisig (processes are etale maps from graphs) and an algebraic semantics in the style of Meseguer and Montanari, in terms of free coloured props, and allows the following unification: for P a Petri net, the Segal space of P-processes is shown to be the free coloured prop-in-groupoids on P. There is also an unfolding semantics à la Winskel, which bypasses the classical symmetry problems: with the new formalism, every Petri net admits a universal unfolding, which in turn has associated an event structure and a Scott domain. Since everything is encoded with explicit sets, Petri nets and their processes have elements. In particular, individual-token semantics is native. (Collectivetoken semantics emerges from rather drastic quotient constructions à la Best–Devillers, involving taking π0 of the groupoids of states [ABSTRACT FROM AUTHOR]

Abstract Fluorinated graphene, a two-dimensional nanomaterial composed of three atomic layers, a central carbon layer sandwiched between two layers of fluorine atoms, has attracted considerable attention across various fields, particularly for its potential use in biomedical applications. Nonetheless, scant effort has been devoted to assessing the potential toxicological implications of this nanomaterial. In this study, we scrutinize the potential impact of fluorinated graphene on a protein model, HP35 by utilizing extensive molecular dynamics (MD) simulation methods. Our MD results elucidate that upon adsorption to the nanomaterial, HP35 undergoes a denaturation process initiated by the unraveling of the second helix of the protein and the loss of the proteins hydrophobic core. In detail, substantial alterations in various structural features of HP35 ensue, including alterations in hydrogen bonding, Q value, and RMSD. Subsequent analyses underscore that hydrophobic and van der Waals interactions (predominant), alongside electrostatic energy (subordinate), exert influence over the adsorption of HP35 on the fluorinated graphene surface. Mechanistic scrutiny attests that the unrestrained lateral mobility of HP35 on the fluorinated graphene nanomaterial primarily causes the exposure of HP35's hydrophobic core, resulting in the eventual structural denaturation of HP35. A trend in the features of 2D nanostructures is proposed that may facilitate the denaturation process. Our findings not only substantiate the potential toxicity of fluorinated graphene but also unveil the underlying molecular mechanism, which thereby holds significance for the prospective utilization of such nanomaterials in the field of biomedicine.

Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms for image reconstruction that leverage the power of modern denoisers for image regularization. In particular, they have been shown to deliver state-of-the-art reconstructions with CNN denoisers. Since the regularization is performed in an ad-hoc manner, understanding the convergence of PnP and RED has been an active research area. It was shown in recent works that iterate convergence can be guaranteed if the denoiser is averaged or nonexpansive. However, integrating nonexpansivity with gradient-based learning is challenging, the core issue being that testing nonexpansivity is intractable. Using numerical examples, we show that existing CNN denoisers tend to violate the nonexpansive property, which can cause PnP or RED to diverge. In fact, algorithms for training nonexpansive denoisers either cannot guarantee nonexpansivity or are computationally intensive. In this work, we construct contractive and averaged image denoisers by unfolding splitting-based optimization algorithms applied to wavelet denoising and demonstrate that their regularization capacity for PnP and RED can be matched with CNN denoisers. To our knowledge, this is the first work to propose a simple framework for training contractive denoisers using network unfolding. [ABSTRACT FROM AUTHOR]

Fluorinated graphene, a two-dimensional nanomaterial composed of three atomic layers, a central carbon layer sandwiched between two layers of fluorine atoms, has attracted considerable attention across various fields, particularly for its potential use in biomedical applications. Nonetheless, scant effort has been devoted to assessing the potential toxicological implications of this nanomaterial. In this study, we scrutinize the potential impact of fluorinated graphene on a protein model, HP35 by utilizing extensive molecular dynamics (MD) simulation methods. Our MD results elucidate that upon adsorption to the nanomaterial, HP35 undergoes a denaturation process initiated by the unraveling of the second helix of the protein and the loss of the proteins hydrophobic core. In detail, substantial alterations in various structural features of HP35 ensue, including alterations in hydrogen bonding, Q value, and RMSD. Subsequent analyses underscore that hydrophobic and van der Waals interactions (predominant), alongside electrostatic energy (subordinate), exert influence over the adsorption of HP35 on the fluorinated graphene surface. Mechanistic scrutiny attests that the unrestrained lateral mobility of HP35 on the fluorinated graphene nanomaterial primarily causes the exposure of HP35's hydrophobic core, resulting in the eventual structural denaturation of HP35. A trend in the features of 2D nanostructures is proposed that may facilitate the denaturation process. Our findings not only substantiate the potential toxicity of fluorinated graphene but also unveil the underlying molecular mechanism, which thereby holds significance for the prospective utilization of such nanomaterials in the field of biomedicine. [ABSTRACT FROM AUTHOR]

The distortions of measured beta spectra are addressed by means of unfolding algorithms. Two different approaches, the Maximum-Likelihood Expectation-Maximization and the Tikhonov regularization, are tested on various simulated spectra, for which the initial spectrum to retrieve is known, and on a 99Tc spectrum measured with our dedicated setup. Statistical uncertainties of distorted measured spectra are propagated by determining the covariance matrices. Both algorithms provide satisfactory results but Tikhonov performs overall better for most of the studied radionuclides. Highlight is made on the necessity to employ at least two independent methods to ensure the accuracy of the unfolded spectra and to estimate the internal bias of each algorithm.

Nanoparticles (NPs) have distinct properties due to their small size and high surface area-to-volume ratio. These characteristics result in unique features not seen in bulk materials. Metals, semiconductors, and polymers are among the many varieties. Nanomaterials are used in various industries, including medical, electronics, and environmental cleanup. However, their potential environmental and health consequences must be carefully considered. Metal nanoparticles (MNPs) have received significant attention due to their outstanding physicochemical features and diverse applications in various sectors. MNPs are utilized in different fields, from electronics to health. Previously, the effect of various metal nanoparticles on the structure, stability, and functionality of metabolic enzymes was investigated. Copper (Cu) NPs were found to have significant negative impacts on these enzymes. The focus of the current investigation turned to serum albumin, a vital plasma protein containing oxidized cysteine residues. The findings demonstrated that NPs had a minor impact on the structure and stability of serum albumin, in contrast to the effects observed on reduced cysteine-containing enzymes.

High hydrostatic pressure (HHP) has gained prominence in the food processing industry over the last decade. In addition to the effectiveness of microbial and enzymatic inactivation, HHP directly impacts protein structures and properties. Accordingly, this review article aims to consolidate relevant research findings elucidating the effects of HHP on protein structure, allergenicity, bioactivities, and functional properties across diverse protein sources. They encompass cereals, legumes, nuts, meat, poultry products, milk, eggs, seafood, algae, insects, seeds, and vegetables. This review provides insights into the consistent trends of HHP effects on each protein source. In conclusion, HHP induces alterations in non-covalent bonds within protein structures, leading to the unfolding of their interior regions and consequential changes in their properties. Remarkably, the allergenicity of cereals, legumes, and nuts decreases while their bioactivities and digestibility escalate. The disruption of non-covalent bonds during HHP results in the exposure of the interior hydrophobic regions to the surface microenvironment, thereby enhancing the surface hydrophobicity of proteins, particularly those derived from seeds and vegetables. HHP weakens the allergenicity and elevates the foaming properties of proteins from dairy products, including improving the gelling properties and antioxidant activities of egg proteins. Texture profiles of meat and poultry, particularly hardness, are enhanced. Furthermore, HHP demonstrates the potential to diminish the allergenicity of seafood proteins and augment insect protein bioactivities. Lastly, HHP enhances the extraction of algal bioactive components, improving their nutritional quality. [ABSTRACT FROM AUTHOR]

Bavinck's critical appropriation of modern grammar in discussing Christian doctrine has led to his being described as a thinker who is "modern yet orthodox." His appropriation of modern philosophy and psychology is clearly seen in his use of 'personality' in his doctrine of God and anthropology. This article argues that his use in these two areas demonstrates the organic ontology of creatures created in the image of their Creator. Thus, in understanding divine personality, human personality comes into focus, both theologically and psychologically. The article concludes with reflections on how the study of personality in Bavinck's writings helps one to understand his entire project. [ABSTRACT FROM AUTHOR]

In this study, we consider algorithm unfolding for the multiple measurement vector (MMV) problem in the case where only few training samples are available. Algorithm unfolding has been shown to empirically speed-up in a data-driven way the convergence of various classical iterative algorithms, but for supervised learning, it is important to achieve this with minimal training data. For this, we consider learned block iterative shrinkage thresholding algorithm (LBISTA) under different training strategies. To approach almost data-free optimization at minimal training overhead, the number of trainable parameters for algorithm unfolding has to be substantially reduced. We therefore explicitly propose a reduced-size network architecture based on the Kronecker structure imposed by the MMV observation model and present the corresponding theory in this context. To ensure proper generalization, we then extend the analytic weight approach by Liu and Chen to LBISTA and the MMV setting. Rigorous theoretical guarantees and convergence results are stated for this case. We show that the network weights can be computed by solving an explicit equation at the reduced MMV dimensions which also admits a closed-form solution. Toward more practical problems, we then considered convolutional observation models and show that the proposed architecture and the analytical weight computation can be further simplified and thus open new directions for convolutional neural networks. Finally, we evaluate the unfolded algorithms in numerical experiments and discuss connections to other sparse recovering algorithms. [ABSTRACT FROM AUTHOR]

Biometrical sciences and disease diagnosis in particular, are often concerned with the analysis of associations for cross‐classified data, for which distance association models give us a graphical interpretation for non‐sparse matrices with a low number of categories. In this framework, usually binary exploratory and response variables are present, with analysis based on individual profiles being of great interest. For saturated models, we show the usual linear relationship for log‐linear models is preserved in full dimension for the distance association parameterization. This enables a two‐step procedure to facilitate the analysis and the interpretation of associations in terms of unfolding after the overall and main effects are removed. The proposed procedure can deal with cross‐classified data for profiles by binary variables, and it is easy to implement using traditional statistical software. For disease diagnosis, the problems of a degenerate solution in the unfolding representation, and that of determining significant differences between the profile locations are addressed. A hypothesis test of independence based on odds ratio is considered. Furthermore, a procedure is proposed to determine the causes of the significance of the test, avoiding the problem of error propagation. The equivalence between a test for equality of odds ratio pairs and the test for equality of location for two profiles in the unfolding representation in the disease diagnosis is shown. The results have been applied to a real example on the diagnosis of coronary disease, relating the odds ratios with performance parameters of the diagnostic test. [ABSTRACT FROM AUTHOR]

In production areas dedicated to the manufacturing of small series or unique products, i.e. in sectors which require frequent calculations for geometric determinations (such as the case of obtaining the unfolded shape of oblique cones frustums from the joints of different tubes (pipes) of varying diameters and/or orientations it is useful to achieve their development within a short timeframe, accurately and with minimal effort. Regarding this, an AutoLisp function was created for the rapid development of an oblique cone frustum, capable of being used in the current working session of the drawing executed in AutoCAD, without requiring additional calculations or drawings [ABSTRACT FROM AUTHOR]

In this study, we consider algorithm unfolding for the multiple measurement vector (MMV) problem in the case where only few training samples are available. Algorithm unfolding has been shown to empirically speed-up in a data-driven way the convergence of various classical iterative algorithms, but for supervised learning, it is important to achieve this with minimal training data. For this, we consider learned block iterative shrinkage thresholding algorithm (LBISTA) under different training strategies. To approach almost data-free optimization at minimal training overhead, the number of trainable parameters for algorithm unfolding has to be substantially reduced. We therefore explicitly propose a reduced-size network architecture based on the Kronecker structure imposed by the MMV observation model and present the corresponding theory in this context. To ensure proper generalization, we then extend the analytic weight approach by Liu and Chen to LBISTA and the MMV setting. Rigorous theoretical guarantees and convergence results are stated for this case. We show that the network weights can be computed by solving an explicit equation at the reduced MMV dimensions which also admits a closed-form solution. Toward more practical problems, we then considered convolutional observation models and show that the proposed architecture and the analytical weight computation can be further simplified and thus open new directions for convolutional neural networks. Finally, we evaluate the unfolded algorithms in numerical experiments and discuss connections to other sparse recovering algorithms.

In production areas dedicated to the manufacturing of small series or unique products, i.e. in sectors which require frequent calculations for geometric determinations (such as the case of obtaining the unfolded shape of oblique cones frustums from the joints of different tubes (pipes) of varying diameters and/or orientations it is useful to achieve their development within a short timeframe, accurately and with minimal effort. Regarding this, an AutoLisp function was created for the rapid development of an oblique cone frustum, capable of being used in the current working session of the drawing executed in AutoCAD, without requiring additional calculations or drawings.

We consider the non-semisimple 0:1 resonance (i.e. the unperturbed equilibrium has two purely imaginary eigenvalues ± i α ( α ∈ R and α > 0) and a non-semisimple double-zero one) Hamiltonian bifurcation with one distinguished parameter, which corresponds to the supercritical Hamiltonian pitchfork bifurcation. Based on BCKV singularity theory established by [H.W. Broer, S. -N. Chow, Y. Kim, and G. Vegter, A normally elliptic Hamiltonian bifurcation, Z. Angew. Math. Phys. 44 (3) (1993), pp. 389–432], this bifurcation essentially triggered by the reversible universal unfolding M = 1 2 p 2 + 1 4 q 4 + (λ + I 1) q 2 with respect to BCKV-restricted morphisms of the planar non-semisimple singularity 1 2 p 2 + 1 4 q 4 (the I 1 is regarded as distinguished parameter with respect to the external parameter λ). We first give the plane bifurcation diagram of the integrable Hamiltonian on each level of integral in detail, which is related to the usual supercritical Hamiltonian pitchfork bifurcation. Then, we use the S 1 -symmetry generated by the additional pair of imaginary eigenvalues ± i α to reconstruct the above plane bifurcation phenomenon caused by the zero eigenvalue pair into the case with two degrees of freedom. Finally, we prove the persistence of typical bifurcation scenarios (e.g. 2-dimensional invariant tori and the symmetric homoclinic orbit) under the small Hamiltonian perturbations, as proposed by [H.W. Broer, S. -N. Chow, Y. Kim, and G. Vegter, A normally elliptic Hamiltonian bifurcation, Z. Angew. Math. Phys. 44 (3) (1993), pp. 389–432]. An example system (the coupled Duffing oscillator) with strong linear coupling and weak local nonlinearity is given for this bifurcation. [ABSTRACT FROM AUTHOR]

In Petri net unfolding, according to the strategies of breadth first and depth first, the biggest problem lies in the potential explosion of the state space. Unfolding generates either accessible trees or branch processes. Making marking reduction or branch cutting accessible proves to be an effective approach to mitigating the state space expansion. In this paper, we propose three reduction rules based on similarity equivalence, conduct state space reduction, present three theorems supported by a case study, and propose a new unfolding algorithm for the unfolding process. In both the new case and the experiments, the completeness, optimality, completeness, and memory and time consumption are reduced by about 60%. [ABSTRACT FROM AUTHOR]

Integrin, as a mechanotransducer, establishes the mechanical reciprocity between the extracellular matrix (ECM) and cells at integrin-mediated adhesion sites. This study used steered molecular dynamics (SMD) simulations to investigate the mechanical responses of integrin αvβ3 with and without 10th type III fibronectin (FnIII10) binding for tensile, bending and torsional loading conditions. The ligand-binding integrin confirmed the integrin activation during equilibration and altered the integrin dynamics by changing the interface interaction between β-tail, hybrid and epidermal growth factor domains during initial tensile loading. The tensile deformation in integrin molecules indicated that fibronectin ligand binding modulates its mechanical responses in the folded and unfolded conformation states. The bending deformation responses of extended integrin models reveal the change in behaviour of integrin molecules in the presence of Mn2+ ion and ligand based on the application of force in the folding and unfolding directions of integrin. Furthermore, these SMD simulation results were used to predict the mechanical properties of integrin underlying the mechanism of integrin-based adhesion. The evaluation of integrin mechanics provides new insights into understanding the mechanotransmission (force transmission) between cells and ECM and contributes to developing an accurate model for integrin-mediated adhesion. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'. [ABSTRACT FROM AUTHOR]

Bremsstrahlung photons measurement show elevated energy emission beyond critical energy in 28-GHz ECR and its nature is not revealed. Thus, the aim of this study is to unveil the nature of high end-point energies at the three measurement locations. Bremsstrahlung photons from a 28-GHz ECR ion source were measured at Busan center of Korean Basic Science Institute (KBSI). The gamma-ray detection system consists of three round type NaI(Tl) scintillation detectors placed 62 cm radially from the beam axis at the three measurement locations namely injection, centre and extraction sides of an ECR ion source, and a NaI(Tl) scintillation detector placed at the view port for monitoring photon intensity along the beam axis. Such design of the experiment was based on the inner structure of the ECR ion source and the shape of the ECR plasma. Bremsstrahlung photons energy spectra were measured at RF power of 1 kW to extract O16 beam with a dominant fraction of O3+ and O4+. Also, bremsstrahlung photons were measured by exchanging the detector positions for studying systematic uncertainties. Monte Carlo simulation based on the Geant4 package was performed to take geometrical acceptance and the energydependent detection efficiency into account due to large non-uniformity in the material budget. True bremsstrahlung energy spectra from the 28-GHz ECR ion source were extracted using an inverse matrix unfolding method. The measured high end-point energies at the three measurement locations were 1.68 MeV, 1.72 MeV and 2.04 MeV for the injection, centre and extraction sides, respectively. The high end-point energy values of the measured bremsstrahlung photons beyond the theoretical energy value of 1.33 MeV were found to be correlated with the structure of the ECR ion source and the shape of the ECR plasma. Furthermore, the high end-point energy values resulting from the bremsstrahlung photons measurements in the 28-GHz ECR plasma are due to the higher number of unconfined electrons arriving at the chamber wall easily, and producing higher bremsstrahlung photons. The unconfined electrons are the results of imperfect magnetic confinement that cause electrons to escape from the plasma volume. This study suggests a new design of magnetic confinement that will not allow an electron to escape. [ABSTRACT FROM AUTHOR]

Protein unfolding is a ubiquitous process responsible for the loss of protein functionality (denaturation), which, in turn, can be accompanied by the death of cells and organisms. The nature of enthalpy–entropy compensation (EEC) in the kinetics of protein unfolding is a subject of debate. In order to investigate the nature of EEC, the "completely loose" transition state (TS) model has been applied to calculate the Arrhenius parameters for the unfolding of polyglycine dimers as a model process. The calculated Arrhenius parameters increase with increasing dimer length and demonstrate enthalpy–entropy compensation. It is shown that EEC results from the linear correlations of enthalpy and entropy of activation with dimer length, which are derived directly from the properties of the transition state. It is shown that EEC in solvated (hydrated, etc.) proteins is a direct consequence of EEC in proteins themselves. The suggested model allows us also to reproduce and explain "exotic" very high values of the pre-exponential factor measured for the proteins unfolding, which are drastically higher than those known for unimolecular reactions of organic molecules. A similar approach can be applied to analyzing the nature of EEC phenomena observed in other areas of chemistry. [ABSTRACT FROM AUTHOR]

Abstract We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.

High hydrostatic pressure (HHP) has gained prominence in the food processing industry over the last decade. In addition to the effectiveness of microbial and enzymatic inactivation, HHP directly impacts protein structures and properties. Accordingly, this review article aims to consolidate relevant research findings elucidating the effects of HHP on protein structure, allergenicity, bioactivities, and functional properties across diverse protein sources. They encompass cereals, legumes, nuts, meat, poultry products, milk, eggs, seafood, algae, insects, seeds, and vegetables. This review provides insights into the consistent trends of HHP effects on each protein source. In conclusion, HHP induces alterations in non-covalent bonds within protein structures, leading to the unfolding of their interior regions and consequential changes in their properties. Remarkably, the allergenicity of cereals, legumes, and nuts decreases while their bioactivities and digestibility escalate. The disruption of non-covalent bonds during HHP results in the exposure of the interior hydrophobic regions to the surface microenvironment, thereby enhancing the surface hydrophobicity of proteins, particularly those derived from seeds and vegetables. HHP weakens the allergenicity and elevates the foaming properties of proteins from dairy products, including improving the gelling properties and antioxidant activities of egg proteins. Texture profiles of meat and poultry, particularly hardness, are enhanced. Furthermore, HHP demonstrates the potential to diminish the allergenicity of seafood proteins and augment insect protein bioactivities. Lastly, HHP enhances the extraction of algal bioactive components, improving their nutritional quality.

Since its introduction in the early 1970s, high pressure crystallography (HPX) has shown great potential for the investigation of different types of matter. Using diamond anvil cells, HPX is an emerging technique that has been rapidly implemented, making it available to biologists, and there is immense potential for utilizing this technique in biological systems in the future. At the molecular level, high-pressure crystallographic investigation provides information on structural characteristics that not only determine the native conformation of a protein but also the conformations with higher free-energy, thus revealing function-related structural changes and properties that can be modified as a result of pressurization. The increase in the number of crystal structures of different macromolecules determined under high pressure over the last five decades can be ascribed mainly to two factors: the emergence of high-pressure cells with very large, open angles, and the advent of third generation synchrotron sources. The use of high pressure crystallography as a research tool has been shown to contribute to the advancements in the basic fields of biochemistry (protein misfolding and aggregation), biophysics (protein stability), and biotechnology (food processing). Presently, with a growing interest in biomedicine and nanotechnology, this nonstandard method appears to be a valid instrument for probing more challenging and complex systems. In this review, we present the method, highlight a selection of recent applications, and describe challenges for high pressure macromolecular crystallography (HPMX). [ABSTRACT FROM AUTHOR]

We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature. [ABSTRACT FROM AUTHOR]

The paper focuses on ratings of the perceived seriousness of crimes and how they can be explained in a testable scaling model (SOCID) that shows commonalities and differences among individuals. The model assumes that crimes are first represented on each person's psychological map. This map can be described as a geometric space spanned by the crimes' perceived attributes. When assessing the seriousness of crimes, the person positions him-/herself on this map by finding a point whose distances to the crime points express how much he/she is rejects the respective crimes. Using three representative German surveys on crime prevention, the SOCID model is positively tested for various predictions: (1) All persons in our surveys share the same psychological map of crimes; (2) a 2-dimensional map accurately describes the persons' observed badness ratings; (3) the points representing the crimes form essentially linear manifolds on the maps; (4) the older and the more conservation-oriented a person, the more similar his/her distances to the crimes; (5) gender has no systematic effect on the distribution of person-points on the crimes' maps. [ABSTRACT FROM AUTHOR]

The issue of forces, the dimensional universal constants, their dimensionless components, marked by the constant ratio of spiral generation, Φ, the golden ratio (1.618), and the feasible existence of a universal principle, are again addressed. As described in earlier articles, Φ is a dimensionless component or feature of most, if not all, of the respective physical, dimensional universal constants. In this rendition, the subject is further elucidated, focusing on various physical theories and their suggested unity through Φ. In this context, the subject is further developed from various perspectives, including epistemological, which serves to bring about more, perhaps deeper insights on the situation. As pointed out, with the exception of the Special Theory of Relativity, and the early stages of quantum mechanics during the early 20th century, modern physical theories have not focused explicitly on any of the dimensional universal constants as center pieces of their theories. With regard to those universal constants, focusing on their dimensionless components, further clarification is intended and illustrated in this article, such as pertaining to wave mechanics and string theory, and their relation to the universal constants. In doing so, one discusses basic questions. Could such universal constants have been proposed or predicted in a physical theory before they were discovered through experiment? Also, can experiments be devised that indicate the feasible existence of a universal principle of completion connected to or executed through the universal constants, that is, through their dimensionless or trans-dimensional features? Can such a principle involve or implicate a deeper ultra-reality of universals? Does such a principle indicate an unfolding of reality, a developmental process, a temporal directionality? In this article, the relevancy of such universal constants, their dimensionless features, marked by Φ, to programmed computer simulations are also shown. These are tangible, visible figures on a computer screen that involve vortical, fractal features, which are connected to Φ and to other dimensionless constants. These are creations due to the human mind's involvement with the mathematical design of computer processes that represent reality. These computer generated figures of developing vortical features, displayed on a computer screen, may be mirrored in a vortical, developing reality of matter-energy and in the higherordered morphologies of organisms. In this connection, based on research, the function of the human brain, and by extension, the mind, appears to be dependent on the anatomical presence of the dimensionless biological constant Φ, the golden ratio. What becomes clear throughout this article, while repeatedly addressing Φ and other dimensionless constants, and referring to various, possibly united, scientific approaches via Φ, is the subtle indication of a universal causation throughout various, natural phenomena on all scales. Such phenomena and their causation, it is feasibly theorized, would be generated, supported, and guided vortically, completely, stably and developmentally by means of the universal constants through their dimensionless features or components, such as Φ and its powers. The various or specific phenomena, or the theories pertaining to such, are thus unified in this manner via Φ. This process of vortical regeneration involving completing forces would occur along with the support of other dimensionless constants such as π and the exponential constant, e. A non-relative, temporal directionality, a guided unfolding, via a guided completion through the universal constants, would be manifest via such a regeneration of completion. In that context, randomness becomes a limiting case, and perhaps, has been, at best, a constructive illusion in the history of science. [ABSTRACT FROM AUTHOR]

We present an improved application of a recently proposed computational method designed to evaluate the change of free energy as a function of the average value of a suitably chosen collective variable in proteins. The method is based on a full atomistic description of the protein and its environment. The goal is to understand how the protein melting temperature changes upon single-point mutations, because the sign of the temperature variation will allow us to discriminate stabilizing vs. destabilizing mutations in protein sequences. In this refined application the method is based on altruistic well-tempered metadynamics, a variant of multiple-walkers metadynamics. The resulting metastatistics is then modulated by the maximal constrained entropy principle. The latter turns out to be especially helpful in free-energy calculations as it is able to alleviate the severe limitations of metadynamics in properly sampling folded and unfolded configurations. In this work we apply the computational strategy outlined above in the case of the bovine pancreatic trypsin inhibitor, a well-studied small protein, which is a reference for computer simulations since decades. We compute the variation of the melting temperature characterizing the folding-unfolding process between the wild-type protein and two of its single-point mutations that are seen to have opposite effect on the free energy changes. The same approach is used for free energy difference calculations between a truncated form of frataxin and a set of five of its variants. Simulation data are compared to in vitro experiments. In all cases the sign of the change of melting temperature is reproduced, under the further approximation of using an empirical effective mean-field to average out protein-solvent interactions.

We treat protein folding as molecular self-assembly, while unfolding is viewed as disassembly. Fracture is typically a much faster process than self-assembly. Self-assembly is often an exponentially decaying process, since energy relaxes due to dissipation, while fracture is a constant-rate process as the driving force is opposed by damping. Protein folding takes two orders of magnitude longer than unfolding. We suggest a mathematical transformation of variables, which makes it possible to view self-assembly as time-reversed disassembly, thus folding can be studied as reversed unfolding. We investigate the molecular dynamics modelling of folding and unfolding of the short Trp-cage protein. Folding time constitutes about 800 ns, while unfolding (denaturation) takes only about 5.0 ns and, therefore, fewer computational resources are needed for its simulation. This RetroFold approach can be used for the design of a novel computation algorithm, which, while approximate, is less time-consuming than traditional folding algorithms.

Logistic regression models are a powerful research tool for the analysis of cross‐classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes these the parameters of interest due to their easy interpretation. In this article we present a distance‐based logistic model that allows a simple graphical interpretation of the association coefficients using the odds ratio in a contingency table. Two configurations are estimated, one for the rows and one for the columns, as the categories of a polytomous predictor and a nominal response variable respectively, such that the local odds ratio and the distances between the predictor and response categories are inversely related. The associations in terms of the odds ratios, or the ratios of the odds to their geometric means, are interpreted through distances for the most common coding schemes of the predictor variable, and the relationship between the distances related to different codings is investigated in its full dimension. The performance of the estimation procedure is analysed with a Monte Carlo experiment. The interpretation of the model and its performance, as well as its comparison with a two‐step procedure involving first a logistic regression and then unfolding, is illustrated using real data sets. [ABSTRACT FROM AUTHOR]

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes. [ABSTRACT FROM AUTHOR]

I propose a fully nonparametric framework for estimation of and inference on the location of political parties in a one-dimensional scale. I derive a novel balance condition sufficient for identification and consistent estimation of the parties' order and a weaker error rate condition that partially identifies the order. Capitalizing on recent advances in testing moment inequalities, I propose a strong and weak specification test of the one-dimensional model. These tests can also be interpreted as tests of a weak sufficient condition for the existence of a majority-core party. Under stronger cardinal assumptions on preferences, I rely on arguments from revealed preference theory to execute similar tests efficiently, obviating the need for confidence set construction using an approximating grid of party locations. I illustrate these techniques using Comparative Study of Electoral Systems survey data on the German party system. [ABSTRACT FROM AUTHOR]

Hyperspectral and multispectral image fusion aims to fuse a low-spatial-resolution hyperspectral image (HSI) and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI. Motivated by the success of model- and deep learning-based approaches, we propose a novel patch-aware deep fusion approach for HSI by unfolding a subspace-based optimization model, where moderate-sized patches are used in both training and test phases. The goal of this approach is to make full use of the information of patch under subspace representation, restrict the scale and enhance the interpretability of the deep network, thereby improving the fusion. First, a subspace-based fusion model was built with two regularization terms to localize pixels and extract texture. Then, the subspace-based fusion model was solved by the alternating direction method of multipliers algorithm, and the model was divided into one fidelity-based problem and two regularization-based problems. Finally, a structured deep fusion network was proposed by unfolding all steps of the algorithm as network layers. Specifically, the fidelity-based problem was solved by a gradient descent algorithm and implemented by a network. The two regularization-based problems were described by proximal operators and learnt by two u-shaped architectures. Moreover, an aggregation fusion technique was proposed to improve the performance by averaging the fused images in all iterations and aggregating the overlapping patches in the test phase. Experimental results, conducted on both synthetic and real datasets, demonstrated the effectiveness of the proposed approach.

[Display omitted] • Refolding of SDS-induced partially-unfolded protein occurs in a reversible manner. • Tetraalkylammonium ionic liquids act as refolding agents. • Intrinsic emission intensity and the average lifetime of the surfactant -induced partially-unfolded protein restores in the native form in the presence of tetraalkylammonium ionic liquid. • The α-helical content of the partially-unfolded protein reverts back substantially close to that of the native protein. • Tetraalkylammonium ionic liquids (ILs) assisted refolding of partially-unfolded protein via formation of mixed micellar aggregate. The current study reports a new strategy for ionic liquid (ILs) assisted refolding of sodium dodecyl sulfate (SDS)-induced partially unfolded protein via mixed micellar aggregate formation between SDS and the ionic liquid. This approach can indeed be utilized to keep many essential and sensitive proteins as active and prolong their shelf life. For SDS-induced partially unfolded proteins, the ionic liquid has been identified to function as a refolding agent. The phenomenon has been observed by us for the first time and we have thoroughly investigated the same utilizing a variety of spectroscopic, microscopic approaches, and attempted to rationalize from computational techniques as well. The steady-state fluorescence emission spectra and time-resolved fluorescence decay show that both the intrinsic emission intensity and the average lifetime, respectively, of the surfactant-induced partially-unfolded protein increase in the presence of ionic liquid, indicating a possible reversal of unfolding (refolding) of the protein (up to ∼95 % revival of luminescence intensity and average lifetime with respect to the native protein). The CD and FTIR results also confirmed that the α-helical content of the pre-unfolded protein reverts substantially and is very close to that of the native protein. To understand the refolding process of SDS-induced partially-unfolded protein, we have further studied the structural recovery of urea-induced pre-denatured protein utilizing the same ionic liquid. Interestingly, no such refolding was observed in this case. From the experimental results, it appears that ionic liquid is involved in scavenging the unfolding surfactant monomers by forming mixed-micellar aggregates where the ion-ion attachment is likely to have impact on the said process. [ABSTRACT FROM AUTHOR]

In this work, neutron spectra are unfolded using artificial neural networks (ANNs). The neutron response of the NE213 scintillator detector, characterized by the pulse height distribution, is calculated to obtain the necessary data for unfolding the energy spectrum. This is achieved using both analytical response functions and response functions generated by the MCNPX-PHOTRACK code. In this query, the Levenberg-Marquardt method (LMM), which has a high computational speed in the learning method, is used to train the network. The performance of the ANN for unfolding the neutron energy spectrum of the NE213 scintillation detector was evaluated by comparing its results to the established Gravel method. The ANN method consistently produced spectra with a single peak closely matching the incident energy, while the Gravel method showed additional peaks and distortions. Quantitative analysis revealed a lower relative energy peak difference (indicating higher accuracy) for the ANN method compared to Gravel, particularly when noise was introduced into the data. These results suggest that ANNs offer a more robust and accurate approach for neutron spectrum unfolding. • The neutron energy spectrum was unfolded using LMBP algorithm. • A two-layer neural network for energy spectrum unfolding was developed. • In the LMBP method, the time and cost of calculations are reduced considerably. • A comparison of LMBP with GRAVEL shows that both methods are very efficient. [ABSTRACT FROM AUTHOR]

Understanding how shear affects whey protein stability is crucial to deal with typical industrial issues occurring at the bulk solution/surface interface, such as fouling during heat treatments. However, at the state of the art, this effect remains unclear, contrary to that of temperature. This article presents a novel strategy to study the impact of shear rate and concentration on the accumulation of whey protein surficial deposits. It consists in applying a range of shear rates (0–200 s−1) at controlled temperature (65 °C) on whey protein solutions (5–10 wt%) by a parallel plate rheometer equipped with a glass disc, thus allowing the off-line characterization of the deposits by microscopy. Our results highlight an unequivocal effect of increasing shear stress. At 5 wt%, it fosters the formation of primary deposits (≈ 10 μm), whereas at 10 wt% it results in the development of complex branched structures (≈ 50 μm) especially for shear rates ranging from 140 s−1 to 200 s−1. Based on the classification by size of the observed populations, we discuss possible hypotheses for the deposit growth kinetics, involving the interplay of different physico-chemical protein-surface interactions and paving the way to future further investigations. • A novel methodology to study how shear rate influences protein surface deposition. • Increasing shear rates lead to enhanced surficial deposits. • Number and morphology of surficial deposits depend on protein concentration. • Thicker branched structures results from the connection of primary deposits. [ABSTRACT FROM AUTHOR]

Our group is currently developing a multilayer neutron spectrometer based on moderator combination selection to evaluate neutron energy spectra in irradiation fields for boron neutron capture therapy (BNCT). In a previous study, we optimally selected (based on simulations) liquid moderator combinations of the spectrometer to achieve high accuracy in the evaluated neutron energy spectra. According to the optimization result, we conducted a spectrometer verification test in the BNCT irradiation field at the Heavy Water Neutron Irradiation Facility of Kyoto University Reactor (KUR-HWNIF). The neutron energy spectrum result was evaluated, and the uncertainty was found to be less than 15%. The experimental results prove that the multilayer neutron spectrometer based on moderator combination selection effectively evaluates the neutron energy spectrum in a BNCT irradiation field. [ABSTRACT FROM AUTHOR]