Stable Model Structure Research Articles

Proper Equivariant Stable Homotopy Theory

This monograph introduces a framework for genuine proper equivariant stable homotopy theory for Lie groups. The adjective ‘proper’ alludes to the feature that equivalences are tested on compact subgroups, and that the objects are built from equivariant cells with compact isotropy groups; the adjective ‘genuine’ indicates that the theory comes with appropriate transfers and Wirthmüller isomorphisms, and the resulting equivariant cohomology theories support the analog of an R O ( G ) R O(G) -grading. Our model for genuine proper G G -equivariant stable homotopy theory is the category of orthogonal G G -spectra; the equivalences are those morphisms that induce isomorphisms of equivariant stable homotopy groups for all compact subgroups of G G . This class of π ∗ \pi _* -isomorphisms is part of a symmetric monoidal stable model structure, and the associated tensor triangulated homotopy category is compactly generated. Consequently, every orthogonal G G -spectrum represents an equivariant cohomology theory on the category of G G -spaces. These represented cohomology theories are designed to only depend on the ‘proper G G -homotopy type’, tested by fixed points under all compact subgroups. An important special case of our theory are infinite discrete groups. For these, our genuine equivariant theory is related to finiteness properties in the sense of geometric group theory; for example, the G G -sphere spectrum is a compact object in our triangulated equivariant homotopy category if the universal space for proper G G -actions has a finite G G -CW-model. For discrete groups, the represented equivariant cohomology theories on finite proper G G -CW-complexes admit a more explicit description in terms of parameterized equivariant homotopy theory, suitably stabilized by G G -vector bundles. Via this description, we can identify the previously defined G G -cohomology theories of equivariant stable cohomotopy and equivariant K-theory as cohomology theories represented by specific orthogonal G G -spectra.

In silico structural analysis of secretory clusterin to assess pathogenicity of mutations identified in the evolutionarily conserved regions

Clusterin (CLU) is a secreted glycoprotein, heterodimeric in nature, and is expressed in a wide variety of tissues and body fluids such as serum and plasma. CLU has also been known to be a promising biomarker for cell death, malignancy, cancer progression, and resistance development. However, the lack of a CLU crystal structure obstructs understanding the possible role of reported mutations on the structure, and the subsequent effects on downstream signaling pathways and cancer progression. Considering the importance of crystal structure, a model structure of the pre-secretory isoform of CLU was built to predict the effect of mutations at the molecular level. Ab initio model was built using RaptorX, and loop refinement and energy minimization were carried out with ModLoop, ModRefiner, and GalaxyWeb servers. The cancer associated mutational spectra of CLU was retrieved from the cBioPortal server and 117 unique missense mutations were identified. Evolutionarily conserved regions and pathogenicity of mutations identified in CLU were analyzed using ConSurf and Rhapsody, respectively. Furthermore, sequence and structure-based mutational analysis were carried out with iSTABLE, DynaMut and PremPS servers. Molecular dynamics simulations were carried out with GROMACS for 50 ns to determine the stability of the wild type and mutant protein structures. A dynamically stable model structure of pre-secretory CLU (psCLU) which has high concurrence with the sequence based secondary structure predictions has been explored. Changes in the intra-atomic interactions and folding pattern between wild type and mutant structures were observed. To our conclusion, eleven mutations with the highest structural and functional significance have been predicted to have pathogenic and deleterious effects. Communicated by Ramaswamy H. Sarma

Evaluation of scientific and technological achievements in colleges based on machine learning and image feature retrieval

At present, experts and scholars have conducted more research on the ability of colleges and universities to transform scientific and technological achievements. However, they pay more attention to the holistic research on the transformation of scientific and technological achievements in colleges and universities across the country, while rarely divide the research objects in detail. In order to improve the evaluation effect of scientific and technological achievements in colleges and universities, this paper builds a university science and technology achievement evaluation system based on machine learning and image feature retrieval on the basis of analyzing the needs of high-tech achievement evaluation. The system has certain flexibility. Moreover, this study selects the appropriate network architecture based on the actual data and mission objectives of the high-tech achievement evaluation. In addition, this paper proposes a FT-GRU model of a gated recurrent unit network incorporating N nearest neighbor text, and a more stable model structure is obtained through system optimization. Finally, this study designs experiments to verify the performance of the model. The research results show that the university science and technology achievement evaluation system based on machine learning and image feature retrieval constructed in this study meets the expected goals and has certain practical significance.

SYMMETRIC OPERADS IN ABSTRACT SYMMETRIC SPECTRA

This paper sets up the foundations for derived algebraic geometry, Goerss–Hopkins obstruction theory, and the construction of commutative ring spectra in the abstract setting of operadic algebras in symmetric spectra in an (essentially) arbitrary model category. We show that one can do derived algebraic geometry a la Toën–Vezzosi in an abstract category of spectra. We also answer in the affirmative a question of Goerss and Hopkins by showing that the obstruction theory for operadic algebras in spectra can be done in the generality of spectra in an (essentially) arbitrary model category. We construct strictly commutative simplicial ring spectra representing a given cohomology theory and illustrate this with a strictly commutative motivic ring spectrum representing higher order products on Deligne cohomology. These results are obtained by first establishing Smith’s stable positive model structure for abstract spectra and then showing that this category of spectra possesses excellent model-theoretic properties: we show that all colored symmetric operads in symmetric spectra valued in a symmetric monoidal model category are admissible, i.e., algebras over such operads carry a model structure. This generalizes the known model structures on commutative ring spectra and$\text{E}_{\infty }$-ring spectra in simplicial sets or motivic spaces. We also show that any weak equivalence of operads in spectra gives rise to a Quillen equivalence of their categories of algebras. For example, this extends the familiar strictification of$\text{E}_{\infty }$-rings to commutative rings in a broad class of spectra, including motivic spectra. We finally show that operadic algebras in Quillen equivalent categories of spectra are again Quillen equivalent. This paper is also available atarXiv:1410.5699v2.

Effective conception of regresion analysis

The paper deals with the efficient conception of regression analysis, when modeling complex systems and processes. The work objective is the use of the method of formalized obtaining of the structure of multifactor regression model and stable estimation of its coefficient for construction of highly precise statistical models of the process of friction of a rubbing pair of a piston-cylinder in the internal combustion engine. It is supposed that the model structure is unknown for the author. Approximation of initial data is performed with the use of polynomial mathematical models. The idea of complete factor experiment is used as a theoretical ground of correct statistical modeling. The use of the complete factor experiment being impossible, it is recommended to use the multifactor regular plans on the basis of ЛПτ of uniformly distributed sequences. An extended conception of orthogonality of the obtained model has been proposed: the experiment design, model structure and structure elements of the model orthogonal to each other. The orthogonal structure of the multifactor statistical model allows obtaining statistically independent estimates of coefficients of the modeled function. Such a structure may be defined unambiguously with statistically significant coefficients. Normalization of orthogonal effects allows obtaining a maximally stable model structure, and, consequently, its coefficients. The solved problem will be well-posed The considered method of formalized obtaining of the structure of multifactor statistical model and stable estimation of its coefficients is used for constructing statistical models of tribotechnical characteristics of friction parameters of a pair cylinder-piston of the internal combustion engine. Three models have been obtained, which are characterized by good quality parameters: adequacy, informativeness, statistical efficiency, stability. The factors in the model structure are presented by orthogonal normalized contrasts. The models application allows analyzing the mechanisms of the going on phenomena and determining optimal operation conditions of the pair of piston-cylinder internal combustion engine. The results of using the extended conception of orthogonality, when constructing the models of friction parameters of the pair piston-cylinder, have confirmed the promising character of application of the considered approach, its efficiency and expediency in the design of regression statistical model of complex systems and processes.

Marine sponge derived natural products as inhibitors of mycothiol-S-conjugate amidase.

Marine sponges have potential sources for secondary metabolites and are considered as a drug treasure house. In this work, 3D model of Mycothiol-S-conjugate amidase (Mca) was determined by comparative homology modeling program MODELLER based on the crystal structure of 1-D-myo-inositol 2-acetamido-2-deoxy-alpha-D-glucopyranoside deacetylase (MshB) from Mycobacterium tuberculosis as a template. The computed model's energy was minimized and validated to obtain a stable model structure. Stable model was used for docking of nineteen bioactive compounds isolated from marine sponges against Mca using AutoDock 4.2. The docked complexes were validated and enumerated based on the AutoDock Scoring function to pick out the best marine inhibitors based on binding energy. Thus from the entire marine compounds which were docked, we got best one (Arenosclerin E) of them with optimal binding energy -13.11 kcal/mol. Further the best-docked complex was analyzed through Python Molecular Viewer software for their interaction studies. The docked protein - inhibitor complex structure was optimized using molecular dynamics simulation for 5 ps with the CHARMM-22 force field using NAMD incorporated in VMD 1.9.2 and then evaluating the stability of complex structure by calculating RMSD values. Thus from the Complex scoring and binding ability its deciphered that this marine derived compound could be promising inhibitor for Mca as drug target yet pharmacological studies have to confirm it.

Equivariant $\Gamma$-spaces

The aim of this note is to provide a comprehensive treatment of the homotopy theory of $\Gamma$-$G$-spaces for $G$ a finite group. We introduce two level and stable model structures on $\Gamma$-$G$-spaces and exhibit Quillen adjunctions to $G$-symmetric spectra with respect to a flat level and a stable flat model structure respectively. Then we give a proof that $\Gamma$-$G$-spaces model connective equivariant stable homotopy theory along the lines of the proof in the non-equivariant setting given by Bousfield and Friedlander. Furthermore, we study the smash product of $\Gamma$-$G$-spaces and show that the functor from $\Gamma$-$G$-spaces to $G$-symmetric spectra commutes with the derived smash product. Finally, we show that there is a good notion of geometric fixed points for $\Gamma$-$G$-spaces.

A Centered Bivariate Spatial Regression Model for Binary Data with an Application to Presettlement Vegetation Data in the Midwestern United States

Spatially structured discrete data arise in diverse areas of application, such as forestry, epidemiology, or soil sciences. Data from several binary variables are often collected at each location. Variation in distributional properties across the spatial domain is of interest. The specific application that motivates our work involves characterizing historical distributions of two species of Oak in the Driftless Area in the Midwestern United States. Scientists are interested in understanding the patterns of interaction between species, as well as their relationships to spatial covariates. Accounting for spatial dependence is not only of inherent interest but also reduces prediction mean squared error, and is necessary for obtaining appropriate measures of uncertainty (i.e., standard errors and confidence intervals). To address the needs of the application, we introduce a centered bivariate autologistic model, which accounts for the statistical dependence in two response variables simultaneously, for the association between them and for the effect of spatial covariates. The model proposed here offers a relatively stable large-scale model structure, with model parameters which can be interpreted in the usual sense across levels of dependence. Since the model allows for separate dependence parameters for each variable, it offers, in essence, the equivalent of a model with a non-separable covariance function. The flexible model framework permits straightforward generalizations to structures with more than two variables, a temporal component, or an irregular lattice domain. Supplementary materials accompanying this paper appear on-line.

<I>In Silico</I> Comparative Modeling of clpP2 Protein from (<I>Mycobacterium tuberculosis</I> H37Rv)

Tuberculosis remains one of the major threats to human health, Mycobacterium tuberculosis , a gram-positive bacterium kills two or three million people per year.ATPdependent Clp protease (ClpP) is a core unit of a major bacterial protease complex. The Mycobacterium tuberculosis (MTB) genome contains two genes coding for putative ClpPs, ClpP1 and ClpP2 respectively, that are likely to play a role in the virulence of the bacterium. ClpP2 employing as a new attractive drug target for that isolates ClpP2 involved in bacteria growth and oxidative stress tolerance. In the present study a complete structural analysis and 3-D modelling of clpP2 protein of Mycobacterium tuberculosis , was carried out. The 214 amino acid sequence of the clpP2 protein was retrieved from NCBI database. Based on the PDB Blast report, PDB file of ClpP from E. coli was used as template for three dimensional structure of the clpP2 protein, was predicted by using the software MODELLER 9v9.The template protein exposed 49% sequence identity with clpp2 protein. The computed model’s energy was minimized and validated using PROCHECK to obtain a stable model structure. Ramachandran plot showed that, fur protein have 94.7% fully allowed region, 5.3% additionally allowed region, 0.0% generously allowed region and 0.0% disallowed region and later then is submitted in Protein Model Database (PMDB-ID: PM0077929) . The model might be elucidated the use in additional functional characterization of this protein.

English

In this work, 3D model of D2 dopamine receptor was determined by comparative homology modeling program MODELLER. The computed model's energy was minimized and validated using PROCHECK and Errat tool to obtain a stable model structure and was submitted in Protein Model Database (PMDB-ID: PM0079251). Stable model was used for molecular docking against Risperidone and their 15 derivatives using AutoDock 4.2, which resulted in energy-based descriptors such as Binding Energy, Ligand Efficiency, Inhib Constant, Intermol energy, vdW + Hbond + desolv Energy, Electrostatic Energy, Total Internal Energy and Torsional Energy. After that, we have built quantitative structure activity relationship (QSAR) model, which was trained and tested on Risperidone and their 15 derivatives having activity value pKi in µM. For QSAR modeling, Multiple Linear Regression model was engendered using energy-based descriptors yielding correlation coefficient r2 of 0.513. To assess the predictive performance of QSAR models, different cross-validation procedures were adopted. Our results suggests that ligand-receptor binding interactions for D2 employing QSAR modeling seems to be a promising approach for prediction of pKi value of novel antagonists against D2 receptor.

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models.

Modelling nitrogen transformations in waters receiving mine effluents

This paper presents a biogeochemical model developed for a clarification pond receiving ammonium nitrogen rich discharge water from the Boliden concentration plant located in northern Sweden. Present knowledge about nitrogen (N) transformations in lakes is compiled in a dynamic model that calculates concentrations of the six N species (state variables) ammonium-N (N am), nitrate-N (N ox), dissolved organic N in water (N org), N in phytoplankton (N pp), in macrophytes (N mp) and in sediment (N sed). It also simulates the rate of 16 N transformation processes occurring in the water column and sediment as well as water–sediment and water–atmosphere interactions. The model was programmed in the software Powersim using 2008 data, whilst validation was performed using data from 2006 to 2007. The sensitivity analysis showed that the state variables are most sensitive to changes in the coefficients related to the temperature dependence of the transformation processes. A six-year simulation of N am showed stable behaviour over time. The calibrated model rendered coefficients of determination (R 2) of 0.93, 0.79 and 0.86 for N am, N ox and N org, respectively. Performance measures quantitatively expressing the deviation between modelled and measured data resulted in values close to zero, indicating a stable model structure. The simulated denitrification rate was on average five times higher than the ammonia volatilisation rate and about three times higher than the permanent burial of N sed and, hence, the most important process for the permanent removal of N. The model can be used to simulate possible measures to reduce the nitrogen load and, after some modification and recalibration, it can be applied at other mine sites affected by N rich effluents.

Molecular Mechanisms of Ca 2+ Selectivity and Mg 2+ Block of NMDA Receptors

Glutamate receptors are membrane proteins activated by the neurotransmitter glutamate that mediate synaptic excitation in the mammalian brain. NMDA receptors constitute a glutamate receptor subfamily specifically activated by N-methyl-d-aspartate. Due to their high Ca2+ permeability and voltage-dependent channel block by Mg2+, NMDA receptors play a central role in development through stabilization of synaptic connections, as well as in learning and memory by mediating many forms of synaptic plasticity. The mechanisms by which the ion channel of NMDA receptors selects Ca2+ for permeation over all other physiological ions, while binding and restricting Mg2+ permeation, are not well understood. We hypothesize that the slightly different radii of Mg2+ and Ca2+ ions result in drastically different free energy barriers for movement of the ions from a binding site in the selectivity filter to water. We applied quantitative theoretical “bottom up” approaches to this complex system by combining methods of computational chemistry, molecular mechanics, and bioinformatics. We performed high accuracy quantum chemical (QC) calculations to determine the energy of the transition state of the ligand exchange reaction that occurs when divalent ions transition from the selectivity filter of NMDA receptors to water. The results of QC calculations are used to parameterize accurately the polarizable molecular mechanics force fields for divalent ion-ligand interactions. Molecular dynamics simulations with polarizable and non-polarizable force fields are used to predict quantitatively ion interactions with the heterogeneous environment of the pore. The structure of the NMDA receptor channel is constructed and refined using a combination of experimental studies, homology modeling, molecular dynamics, and an optimized structure of a Mg2+ - Asn ligand complex. A homology model of NMDA receptors is refined through extensive equilibration and all-atom molecular dynamics simulations on the multi-nanosecond time-scale to acquire a stable model structure.

Coordination properties of warfarin towards Pr(III) predicted from DFT and FT-IR studies

The coordination behavior of warfarin towards Pr(III) in Pr(L) 3·5H 2O complex (L – warfarin) is investigated through molecular modeling at B3LYP/6-31G(d,p) level and consequent exhaustive comparative vibrational analysis of the ligand and the complex. The calculated NPA charges, Fukui functions and MEP values of the anionic ligand in solution pointed out that the oxygen atoms of the deprotonated hydroxyl and the coumarin carbonyl groups are the most probable reactive sites upon coordination. The metal–ligand binding mode of warfarin is predicted through molecular modeling and energy estimation of different Pr(III)–warfarin structures. In the most stable model structure, the ligand–metal binding is realized through the oxygen of the deprotonated OH group and the oxygen of the keto C O group in pseudo-octahedral polyhedron. The suggested metal–ligand binding mode is confirmed by comparative vibrational analysis of the free ligand and various model structures with different metal–ligand binding modes.

Representability theorems for presheaves of spectra

Suppose that M is a simplicial model category and that F is a contravariant simplicial functor defined on M which takes values in pointed simplicial sets. This note displays conditions on the simplicial model category M and the functor F such that F is representable up to weak equivalence. The conditions on F are homotopy coherent versions of the classical conditions for Brown representability, while M should have the fundamental properties of the stable model structure for presheaves of spectra on a Grothendieck site.

A ClpP protein model as tuberculosis target for screening marine compounds.

ATP-dependent Clp protease (ClpP) is a core unit of a major bacterial protease complex employing as a new attractive drug target for that isolates, which are resistant to antibiotics. Mycobacterium tuberculosis, a gram-positive bacterium, is one of the major causes of hospital acquired infections. ClpP in Mycobacterium tuberculosis is usually tightly regulated and strictly requires a member of the family of Clp-ATPase and often further accessory proteins for proteolytic activation. Inhibition of ClpP eliminates these safeguards and start proteolytic degradation. Such uncontrolled proteolysis leads to inhibition of bacterial cell division and eventually cell death. In order to inhibit Clp protease, at first three dimensional structure model of ClpP in Mycobacterium tuberculosis was determined by comparative homology modeling program MODELLER based on crystal structure of the proteolytic component of the caseinolytic Clp protease (ClpP) from E. coli as a template protein and has 55%sequence identity with ClpP protein. The computed model's energy was minimized and validated using PROCHECK to obtain a stable model structure and is submitted in Protein Model Database (PMDB-ID: PM0075741). Stable model was further used for virtual screening against marine derived bioactive compound database through molecular docking studies using AutoDock 3.05. The docked complexes were validated and enumerated based on the AutoDock Scoring function to pick out the best marine inhibitors based on docked Energy. Thus from the entire 186 Marine compounds which were Docked, we got best 5 of them with optimal docked Energy (Ara-A: -14.31 kcal/mol, Dysinosin C: - 14.90kcal/mol, Nagelamide A: -20.49 kcal/mol, Strobilin: -8.02 kcal/mol, Manoalide: -8.81 kcal/mol). Further the five best-docked complexes were analyzed through Python Molecular Viewer software for their interaction studies. Thus from the Complex scoring and binding ability its deciphered that these Marine compounds could be promising inhibitors for ClpP as Drug target yet pharmacological studies have to confirm it.

Homotopy theory of presheaves of Γ-spaces

We consider the category of presheaves of Γ-spaces, or equivalently, of Γ-objects in simplicial presheaves. Our main result is the construction of stable model structures on this category parametrised by local model structures on simplicial presheaves. If a local model structure on simplicial presheaves is monoidal, then the corresponding stable model structure on presheaves of Γ-spaces is monoidal and satisfies the monoid axiom. This allows us to lift the stable model structures to categories of algebras and modules over a monoid.

Simplicial model category structures on the category of chain functors

The model structure on the category of chain functors Ch, developed in [4], has the main features of a simplicial model category structure, taking into account the lack of arbitrary (co-)limits in Ch. After an appropriate tensor and cotensor structure in Ch is established (§1, §3), Quillen’s axiom SM7 is verified in §5 and §6. Moreover, it turns out that in the definition of a simplicial model structure, the category of simplicial sets can be replaced by the category of simplicial spectra endowing Ch with the structure of an approximate simplicial stable model structure (= approximate ss-model structure) (§7). In §8 the model structure on Ch is shown to be proper.

Model structure on operads in orthogonal spectra

We generalize Berger and Moerdijk’s results on axiomatic homotopy theory for operads to the setting of enriched symmetric monoidal model categories, and show how this theory applies to orthogonal spectra. In particular, we provide a symmetric fibrant replacement functor for the positive stable model structure.

Continuous functors as a model for the equivariant stable homotopy category

In this paper, we investigate the properties of the category of equivariant diagram spectra indexed on the category W_G of based G-spaces homeomorphic to finite G-CW-complexes for a compact Lie group G. Using the machinery of Mandell, May, Schwede, and Shipley, we show that there is a "stable model structure" on this category of diagram spectra which admits a monoidal Quillen equivalence to the category of orthogonal G-spectra. We construct a second "absolute stable model structure" which is Quillen equivalent to the "stable model structure". Our main result is a concrete identification of the fibrant objects in the absolute stable model structure. There is a model-theoretic identification of the fibrant continuous functors in the absolute stable model structure as functors Z such that for A in W_G the collection {Z(A smash S^W)} form an Omega-G-prespectrum as W varies over the universe U. We show that a functor is fibrant if and only if it takes G-homotopy pushouts to G-homotopy pullbacks and is suitably compatible with equivariant Atiyah duality for orbit spaces G/H_+ which embed in U. Our motivation for this work is the development of a recognition principle for equivariant infinite loop spaces.