Narrow Results

The paper assesses the ecological approach which challenges the standard concept of scarcity in both neoclassical economics and neo-Darwinian orthodoxy. The focus is on comparative theory analysis with regards to the relevance of matter/energy influx. It should not be surprising, in light of the mathematical structure, that the ideas of optimization of utility in orthodox economics and optimization of fitness in mainstream biology are based on common assumptions. One of the assumptions is that environmental resources are given or exist passively in relation to the actor. Otherwise, the mathematical maximization functions would not be feasible. The orthodox assumption of resources as given for a specific population of firms or organisms need not deny that the environment continuously changes, but the assumption entails that such changes are by-products of the interaction of the population with the environment, the interference of other populations, and other exogenous factors. Thus, the orthodox assumption of resources as given, according to the ecological school, excludes, at first approximation, endogenous factors in the explanation of environmental dynamics or the economy-environment nexus. Inspired by Alfred Lotka's work, the ecological agenda in economics (as led by Daly and Costanza) and in biology (as advocated by Odum, Wicken, Depew, Weber, and Ulanowicz) advance the same alternative to the orthodox axiom concerning resources; viz., resources are a function of the active agent. While such an alternative is very fruitful, it does not go far enough. It has limitations: In economics, the ecological approach generally cannot solve the long-term problem of the asymmetric exchange between human economy and the environment. In the life sciences, the ecological approach, as far as it adheres to reformed Darwinism, faces difficulties with regards to the concepts of organization and the process of development/evolution. These problems call for similar conceptual innovations in both disciplines.

Physicochemcial properties of amino acids are important factors in determining protein structure and function. Most approaches make use of averaged properties over entire domains or even proteins to analyze their structure or function. This level of coarseness tends to hide the richness of the variability in the different properties across functional domains. This paper studies the conservation of physicochemical properties in a functionally similar family of proteins using a novel wavelet-based technique known as multiresolution analysis. Such an analysis can help uncover characteristics that can otherwise remain hidden. We have studied the protein kinase family of sequences and our findings are as follows: (a) a number of different properties are conserved over the functional catalytic domain irrespective of the sequence identities; (b) conservation of properties can be observed at different frequency levels and they agree well with the known structural/functional properties of the subdomains for the protein kinase family; (c) structural differences between the different kinase family members are reflected in the waveforms; and (d) functionally important mutations show distortions in the waveforms of conserved properties. The potential usefulness of the above findings in identifying functionally similar sequences in the twilight and midnight zones is demonstrated through a simple prediction model for the protein kinase family which achieved a recall of 93.7% and a precision of 96.75% in cross-validation tests.

Cryoelectron microscopy (cryoEM) is an experimental technique to determine the three-dimensional (3D) structure of large protein complexes. Currently, this technique is able to generate protein density maps at 6–9 Å resolution, at which the skeleton of the structure (which is composed of α-helices and β-sheets) can be visualized. As a step towards predicting the entire backbone of the protein from the protein density map, we developed a method to predict the topology and sequence alignment for the skeleton helices. Our method combines the geometrical information of the skeleton helices with the Rosetta ab initio structure prediction method to derive a consensus topology and sequence alignment for the skeleton helices. We tested the method with 60 proteins. For 45 proteins, the majority of the skeleton helices were assigned a correct topology from one of our top ten predictions. The offsets of the alignment for most of the assigned helices were within ±2 amino acids in the sequence. We also analyzed the use of the skeleton helices as a clustering tool for the decoy structures generated by Rosetta. Our comparison suggests that the topology clustering is a better method than a general overlap clustering method to enrich the ranking of decoys, particularly when the decoy pool is small.

Residual Dipolar Couplings (RDCs) are a source of NMR data that can provide a powerful set of constraints on the orientation of inter-nuclear vectors, and are quickly becoming a larger part of the experimental toolset for molecular biologists. However, few reliable protocols exist for the determination of protein backbone structures from small sets of RDCs. DynaFold is a new dynamic programming algorithm designed specifically for this task, using minimal sets of RDCs collected in multiple alignment media. DynaFold was first tested utilizing synthetic data generated for the N–H, C_α–H_α, and C–N vectors of 1BRF, 1F53, 110M, and 3LAY proteins, with up to ±1 Hz error in three alignment media, and was able to produce structures with less than 1.9 Å of the original structures. DynaFold was then tested using experimental data, obtained from the Biological Magnetic Resonance Bank, for proteins PDBID:1P7E and 1D3Z using RDC data from two alignment media. This exercise yielded structures within 1.0 Å of their respective published structures in segments with high data density, and less than 1.9 Å over the entire protein. The same sets of RDC data were also used in comparisons with traditional methods for analysis of RDCs, which failed to match the accuracy of DynaFold's approach to structure determination.

The polyproline-II (PPII) structure domain is crucial in organisms’ signal transduction, transcription, cell metabolism, and immune response. It is also a critical structural domain for specific vital disease-associated proteins. Recognizing PPII is essential for understanding protein structure and function. To accurately predict PPII in proteins, we propose a novel method, AAindex-PPII, which only adopts amino acid index to characterize protein sequences and uses a Bidirectional Gated Recurrent Unit (BiGRU)-Improved TextCNN composite deep learning model to predict PPII in proteins. Experimental results show that, when tested on the same datasets, our method outperforms the state-of-the-art BERT-PPII method, achieving an AUC value of 0.845 on the strict data and an AUC value of 0.813 on the non-strict data, which is 0.024 and 0.03 higher than that of the BERT-PPII method. This study demonstrates that our proposed method is simple and efficient for PPII prediction without using pre-trained large models or complex features such as position-specific scoring matrices.

Naturally derived biopolymers have been widely used for biomedical applications such as drug carriers, wound dressings, and tissue engineering scaffolds. Chitosan is a typical polysaccharide of great interest due to its biocompatibility and film-formability. Chitosan membranes with controllable porous structures also have significant potential in membrane chromatography. Thus, the processing of membranes with porous nanoscale structures is of great importance, but it is also challenging and this has limited the application of these membranes to date. In this study, with the aid of a carefully selected surfactant, polyethyleneglycol stearate-40, chitosan membranes with a well controlled nanoscale structure were successfully prepared. Additional control over the membrane structure was obtained by exposing the suspension to high intensity, low frequency ultrasound. It was found that the concentration of chitosan/surfactant ratio and the ultrasound exposure conditions affect the structural features of the membranes. The stability of nanopores in the membrane was improved by intensive ultrasonication. Furthermore, the stability of the blended suspensions and the intermolecular interactions between chitosan and the surfactant were investigated using scanning electron microscope and Fourier transform infrared spectroscopy (FTIR) analysis, respectively. Hydrogen bonds and possible reaction sites for molecular interactions in the two polymers were also confirmed by FTIR analysis.

We have investigated the crystal structure, the microstructural and morphological characteristics, as well as the magnetic properties of Co nanoparticles (NPs) synthesized by a hydrothermal method. A series of samples has been elaborated for different concentrations of sodium hydroxide. The analysis of X-ray diffraction patterns, using two different wavelengths, has evidenced the coexistence of both $\alpha$-Co and $\beta$-Co phases in the samples. The lattice parameter for both phases is in good agreement with those values expected for their bulk Co counterparts; the grain sizes of NPs were found to be dependent on the NaOH concentration. The scanning electron microscope micrographs show that Co NPs are agglomerated forming micrometer-sized entities whose shape evolves, indicating that the synthesis process affects the morphology of the powdered samples. Magnetic measurements indicate that the coercivity is slightly larger, $H_C>200$ Oe, for Co NPs with dendritic-like shape, probably due to an increase in the magnetocrystalline anisotropy.

Magnetometry and atomic force microscopy (AFM) were used to study the magnetic and structural properties of the R–Fe–B-type (R = Y, Nd, Gd, Ho) alloys. The alloys were synthesized by means of induction melting. The nanocrystalline state of the R–Fe–B-type alloys was reached, mainly, by melt spinning (MS). A multistage treatment of R–Fe–B-type alloys, which included severe plastic deformation of melt-quenched ribbons and subsequent heat treatment, was also used. The surface morphology of samples was studied in detail to interpret the observed magnetic hysteresis loops of the samples. It was found that the type of rare earth ion and treatment methods had the most important influence on the microstructure and magnetic properties.

The structural, electronic, magnetic, and optical properties of Au, Cu, Cr, Mn, Co, Ni, and Fe atoms doped 13-atom silver clusters were investigated by the density functional theory (DFT) in the theoretical frame of the generalized gradient approximation (GGA) exchange-collection function. The results show that all the ground state structures of Au, Cu, Cr, Mn, Co, Ni, and Fe atoms doped 13-atom silver clusters are icosahedral, respectively. The Au atom doped on the surface of Ag${}_{13}$ cluster is stable, while other atoms doped in the center of Ag${}_{13}$ cluster are stable. The electronic stability order from high to small is Ag${}_{12}$Cr₁, Ag${}_{12}$Cu₁, Ag${}_{12}$Co₁, Ag${}_{12}$Fe₁, Ag${}_{12}$Au₁, Ag${}_{12}$Mn₁, Ag${}_{12}$Ni₁. Their magnetic moments are not only related to the doping atom but also the doping location of the atom. The magnetic moments of the Cu, Au, Mn, Co, Ni, Fe, and Cr atoms doped in the Ag${}_{13}$ cluster are 5.0, 3.0, 1.0, 3.0, 4.0, 2.0, and 0.0${\mu }_B$, respectively. Compared with the optical absorption spectrum of the Ag${}_{13}$ cluster, the Au, Cr, and Mn atoms doped the Ag${}_{13}$ cluster leading to blue shift, and the Cu, Co, Ni, and Fe atoms doped the Ag${}_{13}$ cluster resulting in red shift. These studies provide a theoretical basis on applications for clusters in electronic, magnetic, and optical devices.