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In this study, optical Bloch equations with and without neighboring hyperfine states near the degenerate two-level system (DTLS) in the challenging case of 85 Rb D2 transition, which involves the Doppler broadening effect, are solved. The calculated spectra agree well with the experimental results obtained based on the coupling-probe scheme with orthogonal linear polarizations of the coupling and probe fields. The mechanisms of electromagnetically induced absorption (electromagnetically induced transparency) for the open F g = 3 → F e = 2 and 3 transitions (open F g = 2 → F e = 2 and 3 transitions) are determined to be the effect of the strong closed F g = 3 → F e = 4 transition line (strong closed F g = 2 → F e = 1 transition line); this finding is based on a comparison between the calculated absorption profiles of the DTLS without neighboring states and those of all levels with neighboring states, depending on the coupling and probe power ratios. Furthermore, based on the aforementioned comparison, the crucial factors that enhance or reduce the coherence effects and lead to the transformation between electromagnetically induced absorption and electromagnetically induced transparency, are (1) the power ratios between the coupling and probe beams, (2) the openness of the excited state, and (3) effects of the neighboring states due to Doppler broadening in a real atomic system. [ABSTRACT FROM AUTHOR]

Rapid advancement in imaging technology generates an enormous amount of heterogeneous medical data for disease diagnosis and rehabilitation process. Radiologists may require related clinical cases from medical archives for analysis and disease diagnosis. It is challenging to retrieve the associated clinical cases automatically, efficiently and accurately from the substantial medical image archive due to diversity in diseases and imaging modalities. We proposed an efficient and accurate approach for medical image modality classification that can used for retrieval of clinical cases from large medical repositories. The proposed approach is developed using transfer learning concept with pre-trained ResNet50 Deep learning model for optimized features extraction followed by linear discriminant analysis classification (TLRN-LDA). Extensive experiments are performed on challenging standard benchmark ImageCLEF-2012 dataset of 31 classes. The developed approach yields improved average classification accuracy of 87.91%, which is higher up-to 10% compared to the state-of-the-art approaches on the same dataset. Moreover, hand-crafted features are extracted for comparison. Performance of TLRN-LDA system demonstrates the effectiveness over state-of-the-art systems. The developed approach may be deployed to diagnostic centers to assist the practitioners for accurate and efficient clinical case retrieval and disease diagnosis. [ABSTRACT FROM AUTHOR]

Intensity ratio of density sensitive emission lines emitted from Fe ions in the extreme ultraviolet region is important for astrophysics applications. We report high-resolution intensity ratio measurements for Fe ions performed at Tokyo EBIT laboratory by employing a flat-field grazing incidence spectrometer. The experimental intensity ratios of Fe X and Fe XII are plotted as a function of electron density for different electron beam currents. The experimental results are compared with the predicted intensity ratios from the model calculations. [ABSTRACT FROM AUTHOR]

We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches. [ABSTRACT FROM AUTHOR]

In comparison to previously known severe respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the newly emerged Omicron (B.1.1.529) variant shows higher infectivity in humans. Exceptionally high infectivity of this variant raises concern of its possible transmission via other intermediate hosts. The SARS-CoV-2 infectivity is established via the association of spike (S) protein receptor binding domain (RBD) with host angiotensin I converting enzyme 2 (hACE2) receptor. In the course of this study, we investigated the interaction between Omicron S protein RBD with the ACE2 receptor of 143 mammalian hosts including human by protein–protein interaction analysis. The goal of this study was to forecast the likelihood that the virus may infect other mammalian species that coexist with or are close to humans in the household, rural, agricultural, or zoological environments. The Omicron RBD was found to interact with higher binding affinity with the ACE2 receptor of 122 mammalian hosts via different amino acid residues from the human ACE2 (hACE2). The rat (Rattus rattus) ACE2 was found to show the strongest interaction with Omicron RBD with a binding affinity of -1393.6 kcal/mol. These distinct strong binding affinity of RBD of Omicron with host ACE2 indicates a greater potential of new host transmissibility and infection via intermediate hosts. Though expected but the phylogenetic position of the mammalian species may not dictate the Omicron RBD binding to the host ACE2 receptor suggesting an involvement of multiple factors in guiding host divergence of the variant. [ABSTRACT FROM AUTHOR]

Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed ' IDM- PhyChm- Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our analysis demonstrates that ensemble-RF, ensemble-SVM and ensemble-KNN are more effective than their individual counterparts. The proposed ' IDM- PhyChm- Ens' method has shown improved performance compared to existing techniques. [ABSTRACT FROM AUTHOR]

Birds have become an increasing concern for ecological preservation and safety. This paper proposes hierarchical architecture of semantic sensing information for bird acoustic data representation in bird ecological environment. This architecture provides various real-time sensing data such as bird calls using acoustic sensors in sensor networks. In this paper, we implement an ontology structure of hierarchical semantic information representation in bird’s ecological environment. Information of this architecture supports to recognize bird calls, identify birds, classify species, and to track a bird behavior in bird ecological environment. All of this would indicate that we suggest relationship between phenomenon data to service/semantic information in bird ecology. [ABSTRACT FROM AUTHOR]

The Ebola outbreak in 2014 caused many infections and deaths. Some literature works have proposed some models to study Ebola virus, such as SIR, SIS, SEIR, etc. It is proved that the fractional order model can describe epidemic dynamics better than the integer order model. In this paper, we propose a fractional order Ebola system and analyze the nonnegative solution, the basic reproduction number R 0 , and the stabilities of equilibrium points for the system firstly. In many studies, the numerical solutions of some models cannot fit very well with the real data. Thus, to show the dynamics of the Ebola epidemic, the Gorenflo–Mainardi–Moretti–Paradisi scheme (GMMP) is taken to get the numerical solution of the SEIR fractional order Ebola system and the modified grid approximation method (MGAM) is used to acquire the parameters of the SEIR fractional order Ebola system. We consider that the GMMP method may lead to absurd numerical solutions, so its stability and convergence are given. Then, the new fractional orders, parameters, and the root-mean-square relative error g (U ∗) = 0.4146 are obtained. With the new fractional orders and parameters, the numerical solution of the SEIR fractional order Ebola system is closer to the real data than those models in other literature works. Meanwhile, we find that most of the fractional order Ebola systems have the same order. Hence, the fractional order Ebola system with different orders using the Caputo derivatives is also studied. We also adopt the MGAM algorithm to obtain the new orders, parameters, and the root-mean-square relative error which is g (U ∗) = 0.2744 . With the new parameters and orders, the fractional order Ebola systems with different orders fit very well with the real data. [ABSTRACT FROM AUTHOR]

The analysis of simple sequence repeats (SSRs) in 98 genomes across four genera of the family Polyomaviridae was performed. The genome size ranged from 3962 (BM87) to 7369 bp (BM85) but maximum genomes were in the range of 5–5.5 kb. The GC% had an average of 42% and ranged between 34.69 (BM95) and 52.35 (BM81). A total of 3036 SSRs and 223 cSSRs were extracted using IMEx with incident frequency from 18 to 56 and 0 to 7, respectively. The most prevalent mono-nucleotide repeat motif was "T" (48.95%) followed by "A" (33.48%). "AT/TA" was the most prevalent dinucleotide motif closely followed by "CT/TC". The distribution was expectedly more in the coding region with 77.6% SSRs of which nearly half were in Large T Antigen (LTA) gene. Notably, most viruses with humans, apes and related species as host exhibited exclusivity of mono-nucleotide repeats in AT region, a proposed predictive marker for determination of humans as host in the virus in course of its evolution. Each genome has a unique SSR signature which is pivotal for viral evolution particularly in terms of host divergence. [ABSTRACT FROM AUTHOR]

In this article, the first and second-order rogue wave solutions are obtained which are localized in both space and time that appear from nowhere and disappear without a trace. The coupled NLSEs with time-dependent coefficients are considered that describe the effects of ultrashort optical pulse propagation in nonlinear optics and quantum physics. The similarity transformation is used to investigate these rational-like (rogue wave) solutions. Moreover, the 3D graphical representations and contour plots have depicted with different parameters of gravity field and external magnetic field. [ABSTRACT FROM AUTHOR]

Organic amendments could be used as alternative to inorganic P fertilisers, but a clear understanding of the relationship among type of P amendment, microbial activity and changes in soil P fractions is required to optimise their use. Two P-deficient soils were amended with farmyard manure (FYM), poultry litter (PL) and biogenic waste compost (BWC) at 10 g dw kg soil and incubated for 72 days. Soil samples were collected at days 0, 14, 28, 56 and 72 and analysed for microbial biomass C, N and P, 0.5 M NaHCO extractable P and activity of dehydrogenase and alkaline phosphomonoesterase. Soil P fractions were sequentially extracted in soil samples collected at days 0 and 72. All three amendments increased cumulative CO release, microbial biomass C, N and P and activity of dehydrogenase and alkaline phosphomonoesterase compared to unamended soils. The increase in microbial biomass C and N was highest with PL, whereas the greatest increase in microbial biomass P was induced with FYM. All three biomass indices showed the same temporal pattern, with the highest values on day 14 and the lowest on day 72. All amendments increased 0.5 M NaHCO extractable P concentrations with the smallest increase with BWC and the greatest with FYM, although more P was added with PL than with FYM. Available P concentrations decreased over time in the amended soils. Organic amendments increased the concentration of the labile P pools (resin and NaHCO-P) and of NaOH-P, but had little effect on the concentrations of acid-soluble P pools and residual P except for increasing the concentration of organic P in the concentrated HCl pool. Resin P and NaHCO-P pools decreased over time whereas NaOH-P and all organic P pools increased. It is concluded that organic amendments can provide P to plants and can stimulate the build-up of organic P forms in soils which may provide a long-term slow-release P source for plants and soil organisms. [ABSTRACT FROM AUTHOR]