Geometrical and sequence characteristics of alpha-helices in globular proteins

Understanding the sequence-structure relationships in globular proteins is important for reliable protein structure prediction and de novo design. Using a database of 1131 alpha-helices with nonidentical sequences from 205 nonhomologous globular protein chains, we have analyzed structural and sequence characteristics of alpha-helices. We find that geometries of more than 99% of all the alpha-helices can be simply characterised as being linear, curved, or kinked. Only a small number of alpha-helices ( approximately 4%) show sharp localized bends in their middle regions, and thus are classified as kinked. Approximately three-fourths (approximately 73%) of the alpha-helices in globular proteins show varying degrees of smooth curvature, with a mean radius of curvature of 65 +/- 33 A; longer helices are less curved. Computation of helix accessibility to the solvent indicates that nearly two-thirds of the helices ( approximately 66%) are largely buried in the protein core, and the length and geometry of the helices are not correlated with their location in the protein globule. However, the amino acid compositions and propensities of individual amino acids to occur in alpha-helices vary with their location in the protein globule, their geometries, and their lengths. In particular, Gln, Glu, Lys, and Arg are found more often in helices near the surface of globular proteins. Interestingly, kinks often seem to occur in regions where amino acids with low helix propensities (e.g., beta-branched and aromatic residues) cluster together, in addition to those associated with the occurrence of proline residues. Hence the propensities of individual amino acids to occur in a given secondary structure depend not only on conformation but also on its length, geometry, and location in the protein globule.     

Establishment of human parotid pleomorphic adenoma cells in culture: morphological and biochemical characterization

This study reports the establishment of alpha-amylase-producing human parotid pleomorphic adenoma cell lines (2HP and 2HP1) which have been maintained in culture for over 1 yr. The procedures required preparation of cellular clumps from tumor tissue and plating them on plasma clot or precoated dishes. During the initial phase of growth they required modified MCDB-153 medium without serum. When cells showed signs of degeneration they were changed to MCDB-153 medium containing first 2% and then 10% heat inactivated fetal bovine serum. Although cells grew well in MCDB-153 containing 10% serum, the epithelial cell morphology was not distinct. Therefore, the growth and morphology of cells grown in MCDB-10% serum were compared with those in RPMI growth medium containing 10% fetal bovine serum and F12 containing 10% agammaglobulin newborn bovine serum. Although the growth of cells was a little slower in F12 medium than those in MCDB and RPMI, the epithelial cell morphology was maintained better than in other growth media. The cells of 2HP and 2HP1 produce low levels of alpha-amylase and relatively high levels of alpha-amylase mRNAs of 1176 and 702 bp and contain neurofilament-160, a neuronal-specific marker. The cells of 2HP1 are tumorigenic when tested in athymic mice, but the cells of 2HP are not. The establishment of amylase-producing human parotid adenoma cell lines of different characteristics in culture provides a new opportunity to study the mechanisms of differentiation and transformation, and regulation of alpha-amylase in these cells.     

Dielectric behaviour of hot pressed AlN ceramic exposed to inorganic acid vapours

Dielectric behaviour of hot pressed AIN ceramic is studied before and after exposing the samples to inorganic acid (HCl and HNO₃) vapours with a specific aim to study the effect of these vapours on the dielectric constant () and dissipation factor (tan ). Four samples having different volume percentage of porosity (0.2 to 15%) are selected for this study. Dielectric dispersion increases after exposing the samples to the above acid vapours. Tan also increases quite appreciably; the increase being more at higher porosity. Recovery studies show that the exposure effect is reversible. The exposure time dependence of and tan indicates that these parameters show a maxima at a particular exposure time. However, no such maxima is observed in the gravimetric measurements. The increase in dielectric parameters after exposure to acid vapours is explained in terms of the ionic conduction due to the dissociation of these vapours in the presence of moisture. The porosity dependence of this effect is discussed in terms of closed and open porosity reported by other workers.     

Interaction behavior of nitrogen in liquid niobium

The kinetics and mechanism of absorption/desorption of nitrogen in liquid Nb were investigated in the temperature range of 2470 °C to 2670 °C in samples levitated in a N₂/Ar stream with various nitrogen partial pressures. The nitrogen solution reaction in liquid Nb was found to be exothermic, with the standard enthalpy and entropy of solution of −236.4 ± 23.3 kJ/mol and −-5.3 ± 8.3 J/K · mol, respectively. Above the threshold flow rate of the N₂/Ar stream, the absorption process was determined to be second order with respect to nitrogen concentration, indicating that the rate-controlling step is either the adsorption of nitrogen molecules on the liquid surface or dissociation of adsorbed nitrogen molecules into surface-adsorbed atoms. The desorption process was found to be second order as well. At lower flow rates, however, the absorption rate was found to depend on the gas-phase mass transfer rate. The rate equation for nitrogen absorption in the range of 2470 °C to 2670 °C is given by with the value ofQ calculated to be −327.2 ± 20.6 kJ/mol, while nitrogen desorption at 2670 °C follows the relation      

Data stream classification with novel class detection: a review,comparison and challenges

Knowledge and Information Systems - Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a...     

Study of automatic text summarization approaches in different languages

Artificial Intelligence Review - Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing...     

Consensus-based data replication protocol for distributed cloud

The Journal of Supercomputing - Data availability ensures efficient data accessibility by the readers anytime and from anywhere. It can be addressed by creating multiple copies of each data file...     

Correction to: EDVWDD: Event‑Driven Virtual Wheel‑based Data Dissemination for Mobile Sink‑Enabled Wireless Sensor Networks

The Journal of Supercomputing - A correction to this paper has been published: https://doi.org/10.1007/s11227-021-03790-9     

High-utility and diverse itemset mining

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.

     

Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.