Detection of gfp expression from gfp-labelled bacteria spot inoculated onto sugarcane tissues

Green fluorescent protein (GFP) as a marker gene has facilitated biological research in plant-microbe interactions. However, there is one major limiting factor in the detection of GFP in living organisms whose cells emit background autofluorescence. In this study, Herbaspirillum sp. B501gfp1 bacterial cells were spot inoculated onto 5 month-old sterile micro-propagated sugarcane tissues to detect if the GFP fluorescence expression could be distinguished from the tissue’s background fluorescence. Stem tissues and leaf sections mounted on glass slides were directly inoculated with a single touch using the tip of a syringe previously dipped into the inoculum containing 108 bacterial cells/ml. We observed that GFP fluorescence could be easily distinguished in the stem than in the leaf tissues. However, the brightness level of the fluorescence varied with time as a result of fluctuations in the bacterial celldensity. The presence of chloroplasts in the leaf tissues of sugarcane requires the use of bright GFP variants when monitoring bacteria-plant interactions using GFP labelled bacteria

Colonization ability of Herbaspirillum spp. B501gfp1 in sugarcane, a non-host plant in the presence of indigenous diazotrophic endophytes

Inoculating sugarcane with a mixture of diazotrophic endophytic bacteria has shown that they can provide substantial amount of biologically fixed nitrogen to the plant. The genera of diazotrophic endophytes previously isolated from sugarcane have been reported associating with other nonleguminousplants showing a broad host range. This study examined the colonization ability of a wild rice isolate, Herbaspirillum spp., in sugarcane plants in the presence of indigenous endophytes using two inoculum concentrations (102 and 108 bacterial cells ml-1). Internal tissue colonization was observed in plants inoculated with both the 102 and 108 B501gfp1 bacterial cells ml-1 inoculum concentrations. However, extensive colonization and higher bacterial numbers were determined only in the basal stem tissues of plants inoculated with the 108 bacterial cells ml-1

Visio-Linguistic Brain Encoding

Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for single mode stimuli: visual (pretrained CNNs) or text (pretrained language models). Few recent papers have also obtained separate visual and text representation models and performed late-fusion using simple heuristics. However, previous work has failed to explore: (a) the effectiveness of image Transformer models for encoding visual stimuli, and (b) co-attentive multi-modal modeling for visual and text reasoning. In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding. Extensive experiments on two popular datasets, BOLD5000 and Pereira, provide the following insights. (1) To the best of our knowledge, we are the first to investigate the effectiveness of image and multi-modal Transformers for brain encoding. (2) We find that VisualBERT, a multi-modal Transformer, significantly outperforms previously proposed single-mode CNNs, image Transformers as well as other previously proposed multi-modal models, thereby establishing new state-of-the-art. The supremacy of visio-linguistic models raises the question of whether the responses elicited in the visual regions are affected implicitly by linguistic processing even when passively viewing images. Future fMRI tasks can verify this computational insight in an appropriate experimental setting.Comment: 18 pages, 13 figure

Uniqueness and examples of compact toric Sasaki-Einstein metrics

In [11] it was proved that, given a compact toric Sasaki manifold of positive basic first Chern class and trivial first Chern class of the contact bundle, one can find a deformed Sasaki structure on which a Sasaki-Einstein metric exists. In the present paper we first prove the uniqueness of such Einstein metrics on compact toric Sasaki manifolds modulo the action of the identity component of the automorphism group for the transverse holomorphic structure, and secondly remark that the result of [11] implies the existence of compatible Einstein metrics on all compact Sasaki manifolds obtained from the toric diagrams with any height, or equivalently on all compact toric Sasaki manifolds whose cones have flat canonical bundle. We further show that there exists an infinite family of inequivalent toric Sasaki-Einstein metrics on $S^5 \sharp k(S^2 \times S^3)$ for each positive integer $k$.Comment: Statements of the results are modifie

Closed conformal Killing-Yano tensor and geodesic integrability

Assuming the existence of a single rank-2 closed conformal Killing-Yano tensor with a certain symmetry we show that there exist mutually commuting rank-2 Killing tensors and Killing vectors. We also discuss the condition of separation of variables for the geodesic Hamilton-Jacobi equations.Comment: 17 pages, no figure, LaTe

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the neuroscience community has contributed several large cognitive neuroscience datasets related to passive reading/listening/viewing of concept words, narratives, pictures and movies. Encoding and decoding models using these datasets have also been proposed in the past two decades. These models serve as additional tools for basic research in cognitive science and neuroscience. Encoding models aim at generating fMRI brain representations given a stimulus automatically. They have several practical applications in evaluating and diagnosing neurological conditions and thus also help design therapies for brain damage. Decoding models solve the inverse problem of reconstructing the stimuli given the fMRI. They are useful for designing brain-machine or brain-computer interfaces. Inspired by the effectiveness of deep learning models for natural language processing, computer vision, and speech, recently several neural encoding and decoding models have been proposed. In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets. Further, we will review popular deep learning based encoding and decoding architectures and note their benefits and limitations. Finally, we will conclude with a brief summary and discussion about future trends. Given the large amount of recently published work in the `computational cognitive neuroscience' community, we believe that this survey nicely organizes the plethora of work and presents it as a coherent story.Comment: 16 pages, 10 figure

Normalization of Off-shell Boundary State, g-function and Zeta Function Regularization

We consider the model in two dimensions with boundary quadratic deformation (BQD), which has been discussed in tachyon condensation. The partition function of this model (BQD) on a cylinder is determined, using the method of zeta function regularization. We show that, for closed channel partition function, a subtraction procedure must be introduced in order to reproduce the correct results at conformal points. The boundary entropy (g-function) is determined from the partition function and the off-shell boundary state. We propose and consider a supersymmetric generalization of BQD model, which includes a boundary fermion mass term, and check the validity of the subtraction procedure.Comment: 21 pages, LaTeX, comments and 3 new references adde

On the Classification of Brane Tilings

We present a computationally efficient algorithm that can be used to generate all possible brane tilings. Brane tilings represent the largest class of superconformal theories with known AdS duals in 3+1 and also 2+1 dimensions and have proved useful for describing the physics of both D3 branes and also M2 branes probing Calabi-Yau singularities. This algorithm has been implemented and is used to generate all possible brane tilings with at most 6 superpotential terms, including consistent and inconsistent brane tilings. The collection of inconsistent tilings found in this work form the most comprehensive study of such objects to date.Comment: 33 pages, 12 figures, 15 table