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AbstractTularemia caused by Gram-negative, coccobacillus bacterium, Francisella tularensis,is a highly infectious zoonotic disease. Human cases have been reported mainly from the United States, Nordic countries like Sweden and Finland, and some European and Asian countries. Naturally, the disease occurs in several vertebrates, particularly lagomorphs. Type A (subspecies tularensis) is more virulent and causes disease mainly in North America; type B (subspecies holarctica) is widespread, while subspecies mediasiaticais present in central Asia. F. tularensisis a possible bioweapon due to its lethality, low infectious dosage, and aerosol transmission. Small mammals like rabbits, hares, and muskrats are primary sources of human infections, but true reservoir of F. tularensisis unknown. Vector-borne tularemia primarily involves ticks and mosquitoes. The bacterial subspecies involved and mode of transmission determine the clinical picture. Early signs are flu-like illnesses that may evolve into different clinical forms of tularemia that may or may not include lymphadenopathy. Ulcero-glandular and glandular forms are acquired by arthropod bite or handling of infected animals, oculo-glandular form as a result of conjunctival infection, and oro-pharyngeal form by intake of contaminated food or water. Pulmonary form appears after inhalation of bacteria. Typhoidal form may occur after infection viadifferent routes. Human-to-human transmission has not been known. Diagnosis can be achieved by serology, bacterial culture, and molecular methods. Treatment for tularemia typically entails use of quinolones, tetracyclines, or aminoglycosides. Preventive measures are necessary to avoid infection although difficult to implement. Research is underway for the development of effective live attenuated and subunit vaccines.

ABSTRACTThere is an urgent need for a safe, efficacious, and cost-effective vaccine for the coronavirus disease 2019 (COVID-19) pandemic caused by novel coronavirus strain, severe acute respiratory syndrome-2 (SARS-CoV-2). The protective immunity of certain types of vaccines can be enhanced by the addition of adjuvants. Many diverse classes of compounds have been identified as adjuvants, including mineral salts, microbial products, emulsions, saponins, cytokines, polymers, microparticles, and liposomes. Several saponins have been shown to stimulate both the Th1-type immune response and the production of cytotoxic T lymphocytes against endogenous antigens, making them very useful for subunit vaccines, especially those for intracellular pathogens. In this review, we discuss the structural characteristics, mechanisms of action, structure–activity relationship of saponins, biological activities, and use of saponins in various viral vaccines and their applicability to a SARS-CoV-2 vaccine.

AbstractThis study proposes an approach to understand the effect of task engagement through integrated analysis of modulations in functional networks and associated changes in their neurovascular underpinnings at every voxel. For this purpose, a novel approach that brings reorganization in acquired task-functional magnetic resonance imaging information based on hemodynamic characteristics of every task stimulus is proposed and validated. At first, modulations in functional networks of visual target detection task were estimated at every voxel through proposed methodology. It revealed task stimulus dependency in the modulation of default mode network (DMN). The DMN modulated as task negative network (TNN) during target stimulus. On the contrary, it was not entirely TNN during nontarget stimulus. The frontal-parietal and visual networks modulated as task positive network during both task stimuli. Further, modulations of neurovascular underpinnings associated with engagement of task were estimated by correlating the hemodynamically reorganized task blood oxygen level dependent information with simultaneously acquired electroencephalography frequency powers. It revealed the strong association of neurovascular underpinnings with their modulation of functional networks and the associated neuronal activity during task engagement. Finally, graph theoretical parameters such as local, global efficiency and clustering coefficient were also measured at the specific regions for validating the results of proposed method. Modulation observed in graph theory measures clearly validated the activation and deactivation of functional networks observed by the proposed method during task engagement. Thus, the voxel-wise estimation of task-related modulation of functional networks and associated neurovascular underpinnings through proposed technique provide better insights into neuronal mechanism involved during engagement in a task.

Temporal analysis of global cortical communication of cognitive tasks in coarse EEG information is still challenging due to the underlying complex neural mechanisms. This study proposes an attention-based time-series deep learning framework that processes fMRI functional connectivity optimized quasi-stable frequency microstates for classifying distinct temporal cortical communications of the cognitive task. Seventy volunteers were subjected to visual target detection tasks, and their electroencephalogram (EEG) and functional MRI (fMRI) were acquired simultaneously. At first, the acquired EEG information was preprocessed and bandpass to delta, theta, alpha, beta, and gamma bands and then subjected to quasi-stable frequency-microstate estimation. Subsequently, time-series elicitation of each frequency microstates is optimized with graph theory measures of simultaneously eliciting fMRI functional connectivity between frontal, parietal, and temporal cortices. The distinct neural mechanisms associated with each optimized frequency-microstate were analyzed using microstate-informed fMRI. Finally, these optimized, quasi-stable frequency microstates were employed to train and validate the attention-based Long Short-Term Memory (LSTM) time-series architecture for classifying distinct temporal cortical communications of the target from other cognitive tasks. The temporal, sliding input sampling windows were chosen between 180 to 750 ms/segment based on the stability of transition probabilities of the optimized microstates. The results revealed 12 distinct frequency microstates capable of deciphering target detections' temporal cortical communications from other task engagements. Particularly, fMRI functional connectivity measures of target engagement were observed significantly correlated with the right-diagonal delta (r= 0.31), anterior–posterior theta (r= 0.35), left–right theta (r= − 0.32), alpha (r= − 0.31) microstates. Further, neuro-vascular information of microstate-informed fMRI analysis revealed the association of delta/theta and alpha/beta microstates with cortical communications and local neural processing, respectively. The classification accuracies of the attention-based LSTM were higher than the traditional LSTM architectures, particularly the frameworks that sampled the EEG data with a temporal width of 300 ms/segment. In conclusion, the study demonstrates reliable temporal classifications of global cortical communication of distinct tasks using an attention-based LSTM utilizing fMRI functional connectivity optimized quasi-stable frequency microstates.

A robust phenotypic plasticity to ward off adverse environmental conditions determines performance and productivity in crop plants. Flax (linseed), is an important cash crop produced for natural textile fiber (linen) or oilseed with many health promoting products. This crop is prone to drought stress and yield losses in many parts of the world. Despite recent advances in drought research in a number of important crops, related progress in flax is very limited. Since, response of this plant to drought stress has not been addressed at the molecular level; we conducted microarray analysis to capture transcriptome associated with induced drought in flax. This study identified 183 differentially expressed genes (DEGs) associated with diverse cellular, biophysical and metabolic programs in flax. The analysis also revealed especially the altered regulation of cellular and metabolic pathways governing photosynthesis. Additionally, comparative transcriptome analysis identified a plethora of genes that displayed differential regulation both spatially and temporally. These results revealed co-regulated expression of 26 genes in both shoot and root tissues with implications for drought stress response. Furthermore, the data also showed that more genes are upregulated in roots compared to shoots, suggesting that roots may play important and additional roles in response to drought in flax. With prolonged drought treatment, the number of DEGs increased in both tissue types. Differential expression of selected genes was confirmed by qRT-PCR, thus supporting the suggested functional association of these intrinsic genes in maintaining growth and homeostasis in response to imminent drought stress in flax. Together the present study has developed foundational and new transcriptome data sets for drought stress in flax.

Effect of laser pulse in suppressing the dissociation of a molecule is shown. The time dependent Schrödinger is solved numerically for a molecule in an intense laser pulse whose uppermost state is connected to continuum.

A theory for upper hybrid wave induced by relativistic electron beam in magnetized plasma emits tuneable and coherent terahertz radiation. The nonlinear interaction with REB is used to generate terahertz radiation. The enhancement in the amplitude of THz wave is also observed when pre-bunched REB is used. The ponderomotive force applied on beam electrons due to radiation wave and upper wave wiggler modifies the dispersion relation. By solving the dispersion relation, we have derived the growth rate of the radiation wave. Numerical studies indicate that by increasing the beam energy the growth rate of the radiation wave decreases, while it increases with wiggler frequency. Besides this, the growth rate of the radiation wave increases with beam density and decreases with radiation frequency and static magnetic field.

Laser assisted inelastic scattering of heavy ions by alkali atoms is studied theoretically. The non-perturbative quasi-energy method, generalised for many states, is used to describe the laser-atom interaction, and the close coupling method using the impact parameter method is used for scattering calculations. We have calculated the transition probabilities and total cross section for the excitation of alkali atoms, due to simultaneous proton-photon collisions. We show the effect of laser and collision parameters, e.g. laser intensity, impact parameter, laser frequency, on the excitation process.