85 research outputs found
The Tale of a Choreographer, Her student, River and an endangered Heritage: Indu Mitha’s Qaseeda-i-Ilm of Jamal/ “An Ode to Wisdom and Beauty”
No full textChoreographing in Pakistan since the 50’s, the country’s senior most 90+ years young classical dance maestro Indu Mitha has made trailblazing contributions within the Kalakshtera Bharata Natyam using North Indian music, interesting and contemporary content, while also producing more tableau forms of dance.
In one of her recent solo pieces in the later style, titled “Qaseeda-i-Ilm of Jamal” or “An Ode to Wisdom and Beauty” Indu engages with symbolisms of a Hindu goddess of knowledge and Aesthetics_Saraswati and pays tribute to a forgotten dried up river of the same name. Indu Mitha allows the author, for whom and on whose body the dance is made, to bring in the forgotten river in her engagement with people’s histories of the land of present-day Pakistan and eventually facilitates her accessing of and embodying a pluralistic space of inter faith harmony which was occluded
A novel load balanced energy conservation approach in WSN using biogeography based optimization
No full textClustering sensor nodes is an effective technique to reduce energy consumption of the sensor nodes and maximize the lifetime of Wireless sensor networks. Balancing load of the cluster head is an important factor in long run operation of WSNs. In this paper we propose a novel load balancing approach using biogeography based optimization (LB-BBO). LB-BBO uses two separate fitness functions to perform load balancing of equal and unequal load respectively. The proposed method is simulated using matlab and compared with existing methods. The proposed method shows better performance than all the previous works implemented for energy conservation in WSN
A short note on Chinae Hans Muscuovy ducks in Bengal, India
No full textSummaryMuscuovy ducks (Cairina moschata) are popular as a source of poultry meat. Reports on availability of Muscovies (in the free range system of management) in the eastern region of Indu is lacking. In the West Bengal state of Indu two strains of Muscovies have been identified, the strains resemble Black Muscuovy L 303 and White Muscuovy ducks. Ironically they are known as Chinae haras (Chinese duck). Presently a detailed study is being conducted by the author and is being used to develop a strain of broader duck (mule duck) in the region. Reports on availability of Muscovies in this part of the subcontinent are lacking.</jats:p
Determining epitope specificity of T-cell receptors with Transformers
No full textTransformers have dominated the field of natural language processing due to their competency in learning complex relationships within a sequence. Reusing a pre-trained transformer for a downstream task is known as Trans-fer learning. Transfer learning restricts the transformer to a fixed vocabulary; modification in transformer implementation will extend the utility of the transformer. Implementing transformers for complex biological problems can be beneficial in addressing the complexities in the biological sequences. One such biological problem is to capture the specificity of diverse T-cell repertoire to the unique antigens (i.e., immunogenic pathogenic elements). Using transformers to assess the relationship between T-cell receptors and antigen at the sequence level can provide us with better insights into the processes involved in these precise and complex immune responses in humans and murine. In this work, we determined the specificity of multiple TCR to unique antigens by classifying the CDR3 re-gions of TCR sequences to a particular antigen. For this problem, we used three pre-trained auto-encoder (ProtBERT, ProtALBERT, ProtELECTRA) and one pre-trained auto-regressive (ProtXLNet) transformer model wherein, to adapt to the challenges of the complex biological problem at hand, we implemented modifications in the transformers chosen here. We used the VDJdb to obtain the biological data for training and testing the selected transformers. After pre-processing data, we predicted the TCR specificity for 25 antigens (classes) in a multi-class setting. Transformers could predict the specificity of TCRs to an antigen with just the CDR3 sequences from the TCRB chain (weighted F1 score 0.48), the data that was unseen by the transformers. With additional features incorpo-rated, i.e., gene names for TCRs, the weighted F1 improved to 0.55 in the best performing transformer. We demon-strated that different modifications in transformers recognized out-of-vocabulary features with these results. When com-paring the AUC from the transformer model to other previously developed methods for the same biological problem such as TCRGP, TCRDist and DeepTCR, we observed that the transformers outperformed the previously available methods. To exemplify, the MCMV epitope family that suffered from restricted performance in TCRGP due to fewer training samples (~100 samples) showed 10% improvement in AUC with transformers under similar training samples. Transformer's proficiency in learning from fewer data combined with holistic modifications in transformers implementations proves that we can extend its capabilities to explore other biological settings. Further ingenuity in utiliz-ing the full potential of transformers either through attention head visualization or introducing additional features can fur-ther extend T-cell research avenues.Computer Science | Data Science and Technolog
Synthesis of non-hydrazine solution processed Cu2(ZnSn)S4 thin films for solar cells applications
No full textDoes History Matter Only When it Matters Little? The Case of City-Indu try Location
No full textWhen will an industry subject to agglomeration economies move from an old, high-cost site to a new, low-cost site? It is argued that history, in the form of sunk costs resulting from the operation of many firms at a site, creates a first-mover disadvantage that can prevent relocation. It is demonstrated that developers of industrial parks can partly overcome this inertia through discriminatory pricing of land over time, and empirical evidence is provided that they actually engage in such behavior. It is also shown that other aspects of developer land-sale strategy can be a source of information on the nature of interfirm externalities.
A Hybrid Filtering Approach of Digital Video Stabilization for UAV Using Kalman and Low Pass Filter
No full textAbstractIn this paper a new video stabilization algorithm for unmanned aerial vehicles (UAV) has been presented which is used to stabilize the video being transmitted from UAV to the ground station. First, the corner points are extracted using Good Features to Track corner detection algorithm and the extracted points are used to compute the optical flow between two consecutive frames. Next, the points detected from optical flow are used to estimate the motion parameters using an affine transform model. Subsequently, a hybrid filter consisting of Kalman and low pass filter is used to smooth the estimated motion parameters and the frames are warped using the smoothed parameters to obtain a stabilized video sequence. The experimental results show that the algorithm can remove the unwanted vibration more effectively than the one that only uses either a Kalman Filter or a low pass filter
Determining epitope specificity of T-cell receptors with transformers
No full textSUMMARY: T-cell receptors (TCRs) on T cells recognize and bind to epitopes presented by the major histocompatibility complex in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCRs and epitopes. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pre-trained on a large set of protein sequences outperform current strategies. We compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, and ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models. Of all 12 transformer implementations (four models with three different modifications), a modified version of the ProtXLNet model could predict TCR-epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRdist, and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels. We show that the proficiency of transformers in attention learning can be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can extend T-cell research avenues. AVAILABILITY AND IMPLEMENTATION: Data and code are available on https://github.com/InduKhatri/tcrformer.Pattern Recognition and Bioinformatic
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