Association of 16S and 23S ribosomal RNAs to form a bimolecular complex

Association of the 30S and 50S subunits to generate the 70S ribosomes of Escherichia coli has long been known but the mechanism of this interaction remains obscure. Light-scattering studies indicate that naked 16S and 23S RNAs can also associate under conditions similar to those required for the assembly of ribosomes from the constituent RNAs and proteins. The RNA-RNA association also takes place in the presence of ethanol, which promotes folding of 16S and 23S RNAs into specific compact structures with the morphological features of 30S and 50S ribosomes, respectively. Equimolar amounts of the two RNAs are involved in the association. The formation of a stoichiometric complex was shown by light scattering, sucrose density gradient centrifugation, and composite polyacrylamide/agarose gel electrophoresis. The presence of the two species of RNA in the complex was also shown by gel electrophoresis. The association of naked 16S and 23S RNAs suggests that RNA-RNA interaction may play an important role in the association of 30S and 50S subunits

Theoretical considerations for substitutions in alloy steels

THE development of materials possessing certain specific properties was followed on empirical basis in the past. This was specially true in alloy steels, where there are plenty of complications and variables, each effecting the properties sought in its own way. As the theoretical background of this behaviour was least understood, development of materials in the nine-teenth and early twentieth century was based on very laborious approach which was time consuming on the first hand and costly on the other hand. Quite lately attempts have been made to rationalise the metal science on more scientific basis, and although the dream of the designing alloys by slide rule, posse-ssing the required properties, is still far from being achieved, the production costs and wastage in time can be avoided to a considerable extent by it proper application of the factors which have been understood lately. It is the object of this paper to describe these factors in detail so that the attempts in future will he based on more realistic and scientific lines than hitherto followed

Evaluation of the Immunoprotective Potential of Recombinant Paraflagellar Rod Proteins of Trypanosoma evansi in Mice

Trypanosomosis, caused by Trypanosoma evansi, is an economically significant disease of livestock. Systematic antigenic variation by the parasite has undermined prospects for the development of a protective vaccine that targets the immunodominant surface antigens, encouraging exploration of alternatives. The paraflagellar rod (PFR), constituent proteins of the flagellum, are prominent non-variable vaccine candidates for T. evansi owing to their strategic location. Two major PFR constituent proteins, PFR1 (1770bp) and PFR2 (1800bp), were expressed using Escherichia coli. Swiss albino mice were immunized with the purified recombinant TePFR1 (89KDa) and TePFR2 (88KDa) proteins, as well as with the mix of the combined proteins at equimolar concentrations, and subsequently challenged with virulent T. evansi. The PFR-specific humoral response was assessed by ELISA. Cytometric bead-based assay was used to measure the cytokine response and flow cytometry for quantification of the cytokines. The recombinant TePFR proteins induced specific humoral responses in mice, including IgG1 followed by IgG2a and IgG2b. A balanced cytokine response induced by rTePFR 1 and 2 protein vaccination associated with extended survival and improved control of parasitemia following lethal challenge. The observation confirms the immunoprophylactic potential of the covert antigens of T. evansi

Examining Intended Consequences of MGNREGP Intervention on Women Empowerment: Evidences from Block Level Study in Jodhpur District of Rajasthan

Based on the primary data collected from 180 households, the present study investigated the impact of Mahatma Gandhi National Rural Employment Guarantee Programme (MGNREGP) on women empowerment in Jodhpur district of Rajasthan. Besides, the paper looked into the extent of participation of marginalized sections of the society in the programme and intra-household effect of MGNREGP income.The result of z-statistic revealed the significance difference in the extent of employment of male and female workers and women have more than three-fourth (76.50 %) share in workforce in MGNREGP in the district. The programme proved to be gender-friendly and has benefitted the participating women,both tangibly and intangibly, to a large extent by accentuating their choices and capabilities for investments on better food baskets, children's education as well as increased their bargaining power in household's decision process

Approaching human 3D shape perception with neurally mappable models

Humans effortlessly infer the 3D shape of objects. What computations underlie this ability? Although various computational models have been proposed, none of them capture the human ability to match object shape across viewpoints. Here, we ask whether and how this gap might be closed. We begin with a relatively novel class of computational models, 3D neural fields, which encapsulate the basic principles of classic analysis-by-synthesis in a deep neural network (DNN). First, we find that a 3D Light Field Network (3D-LFN) supports 3D matching judgments well aligned to humans for within-category comparisons, adversarially-defined comparisons that accentuate the 3D failure cases of standard DNN models, and adversarially-defined comparisons for algorithmically generated shapes with no category structure. We then investigate the source of the 3D-LFN's ability to achieve human-aligned performance through a series of computational experiments. Exposure to multiple viewpoints of objects during training and a multi-view learning objective are the primary factors behind model-human alignment; even conventional DNN architectures come much closer to human behavior when trained with multi-view objectives. Finally, we find that while the models trained with multi-view learning objectives are able to partially generalize to new object categories, they fall short of human alignment. This work provides a foundation for understanding human shape inferences within neurally mappable computational architectures and highlights important questions for future work

PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment

Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentation-based frame alignment pipeline. The uncertainty-aware planner propagates uncertainty associated with the future motion of detected obstacles, and by incorporating this propagated uncertainty into optimization constraints, the planner effectively navigates around obstacles. Unlike conventional methods that emphasize explicit obstacle tracking, our approach integrates implicit tracking. Sharing trajectories between agents can cause potential collisions due to frame misalignment. Addressing this, we introduce a novel frame alignment pipeline that rectifies inter-agent frame misalignment. This method leverages a zero-shot image segmentation model for detecting objects in the environment and a data association framework based on geometric consistency for map alignment. Our approach accurately aligns frames with only 0.18 m and 2.7 deg of mean frame alignment error in our most challenging simulation scenario. In addition, we conducted hardware experiments and successfully achieved 0.29 m and 2.59 deg of frame alignment error. Together with the alignment framework, our planner ensures safe navigation in unknown environments and collision avoidance in decentralized settings.Comment: 7 pages, 13 figures, conference pape

{PIE}: {P}ortrait Image Embedding for Semantic Control

Editing of portrait images is a very popular and important research topic with a large variety of applications. For ease of use, control should be provided via a semantically meaningful parameterization that is akin to computer animation controls. The vast majority of existing techniques do not provide such intuitive and fine-grained control, or only enable coarse editing of a single isolated control parameter. Very recently, high-quality semantically controlled editing has been demonstrated, however only on synthetically created StyleGAN images. We present the first approach for embedding real portrait images in the latent space of StyleGAN, which allows for intuitive editing of the head pose, facial expression, and scene illumination in the image. Semantic editing in parameter space is achieved based on StyleRig, a pretrained neural network that maps the control space of a 3D morphable face model to the latent space of the GAN. We design a novel hierarchical non-linear optimization problem to obtain the embedding. An identity preservation energy term allows spatially coherent edits while maintaining facial integrity. Our approach runs at interactive frame rates and thus allows the user to explore the space of possible edits. We evaluate our approach on a wide set of portrait photos, compare it to the current state of the art, and validate the effectiveness of its components in an ablation study