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Molecular Biology; Cellular Neuroscience; Immunology; Cell Biology © 2020 The Author(s) Dopamine (DA) plays many roles in the brain, especially in movement, motivation, and reinforcement of behavior; however, its role in regulating innate immunity is not clear. Here, we show that DA can induce DNA-based extracellular traps in primary, adult, human microglia and BV2 microglia cell line. These DNA-based extracellular traps are formed independent of reactive oxygen species, actin polymerization, and cell death. These traps are functional and capture fluorescein (FITC)-tagged Escherichia coli even when reactive oxygen species production or actin polymerization is inhibited. We show that microglial extracellular traps are present in Glioblastoma multiforme. This is crucial because Glioblastoma multiforme cells are known to secrete DA. Our findings demonstrate that DA plays a significant role in sterile neuro-inflammation by inducing microglia extracellular traps. © 2020 The Author(s)

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Department of Computer Science & Engineering

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iScience

Dopamine induces functional extracellular traps in microglia

This article defines a new methodology for pre-recognition of events with object motion analysis in a video without any prior knowledge. This unsupervised application is named as ‘conceptualization’. This conceptualization technique is also tested with real-time video data in an internet of things (IoT) architecture. The merits of rough sets in the framework of granular computing are explored to execute the task. The proposed method is designed for the video sequences that are acquired by simple static RGB sensors. Here the video sequences are granulated with our newly defined ‘motion granules’ and then those are modeled as rough sets over this granulation for moving object/ background estimation. Video conceptualization is performed afterwards by quantifying the approximation with a new measure, namely, motion entropy. The values obtained by this measure reflect the amount of uncertainty present in the motion of each individual moving object which enables precognition of events. The effectiveness of the proposed method is verified with extensive experiments in identifying the different motion patterns present in a video sequence. The frames with possibilities of events present therein are identified with this analysis. Both offline and real-time sequences are used for this verification. An IoT architecture is formed to test the proposed algorithm with physical devices in identifying the frames containing possible events. © 2020

Information Sciences

Rough video conceptualization for real-time event precognition with motion entropy

To increase the recovery of fertilizer nitrogen, reduction in the leaching loss of nitrate-N became a major challenge in nitrogen management in soil. A greenhouse experiment was conducted with maize to study the effect of dose of neem-leaf dust on transformation of nitrogen and the period of efficacy as nitrification inhibitor in a Vertic Epiaquept. The treatments comprised of neem-leaf dust @ 0, 500, 750 and 1000 mg kg−1 of soil at three frequencies, e.g., once at basal (0), in two equal splits at basal and 30 days after sowing and in three equal splits at 0, 30 and 60 days after sowing. Higher values of ammonium-N (7.2–10%) at all stages of sampling in neem-leaf treated soil than their corresponding control proved neem leaf as an effective nitrification inhibitor. Barring few exceptions, transformation/decrease in amino acid-, hexosamine-, total hydrolysable- and total-N was governed by the rate and frequency of neem-leaf dust application. Irrespective of dose and frequency of neem-leaf application there was a net build up of total non-hydrolysable-N. Both dose and frequency of neem-leaf dust application independently had a significant positive impact on dry matter yield and nitrogen uptake by maize. Application of neem-leaf dust at the rate of 600 mg kg−1 of soil in two equal splits was found to be the best combination for dry matter production as well as nitrogen nutrition of maize. © 2020, NAAS (National Academy of Agricultural Sciences).

Agricultural Research

Effect of Dose and Residence Time of Neem-Leaf Dust as Nitrification Inhibitor on N-Transformation in Maize Soil

The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers. Since deep learning is computationally intensive, whereas granular computing, on the other hand, leads to computation gain, a judicious integration of their merits is made so as to make the learning mechanism computationally efficient. Further, it is shown how the concept of z-numbers can be used to quantify the abstraction of semantic information in interpreting a scene, where subjectivity is of major concern, through recognition of its constituting objects. The system, thus developed, involves recognition of both static objects in the background and moving objects in foreground separately. Rough set theoretic granular computing is adopted where rough lower and upper approximations are used in defining object and background models. During deep learning, instead of scanning the entire image pixel by pixel in the convolution layer, we scan only the representative pixel of each granule. This results in a significant gain in computation time. Arbitrary-shaped and sized granules, as expected, perform better than regular-shaped rectangular granules or fixed-sized granules. The method of tracking is able to deal efficiently with various challenging cases, e.g., tracking partially overlapped objects and suddenly appeared objects. Overall, the granulated system shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition. The concept of using Z-numbers, in providing a granulated linguistic description of a scene, is unique. This gives a more natural interpretation of object recognition in terms of certainty toward scene understanding. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Neural Computing and Applications

Granulated deep learning and Z-numbers in motion detection and object recognition

This paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets. A neighborhood rough filter is designed in this process for initial labeling of continuous moving object(s) even in the presence of several variations in different feature spaces. The locations and color models of the object(s) are estimated using their lower-upper approximations in spatio-color neighborhood granular space. Velocity neighborhood granules and acceleration neighborhood granules are then defined over this estimation to predict the object location in the next frame and to speed up the tracking process. A novel concept, namely, intuitionsistic entropy is introduced here, which consists of two new measures: neighborhood rough entropy and neighborhood probabilistic entropy to deal with the ambiguities that arise due to occurrence of overlapping/ occlusion in a video sequence. The unsupervised method of tracking is equally good even when compared with some of the state-of-the art partially supervised methods while showing superior performance during total occlusion. © 1993-2012 IEEE.

IEEE Transactions on Fuzzy Systems

Neighborhood rough filter and intuitionistic entropy in unsupervised tracking

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.

IEEE transactions on cybernetics

Granular Flow Graph, Adaptive Rule Generation and Tracking

This paper deals with several new methodologies and concepts in the area of rough set theoretic granular computing which are then applied in video tracking. A new concept of neighborhood granule formation over images is introduced here. These granules are of arbitrary shapes and sizes unlike other existing granulation techniques and hence more natural. The concept of rough-rule base is used for video tracking to deal with the uncertainties and incompleteness as well as to gain in computation time. A new neighborhood granular rough rule base is formulated which proves to be effective in reducing the indiscernibility of the rule-base. This new rule-base provides more accurate results in the task of tracking. Two indices to evaluate the performance of tracking are defined. These indices do not need ground truth information or any estimation technique like the other existing ones. All these features are demonstrated with suitable experimental results. © 2015, Springer Science+Business Media Dordrecht.

Natural Computing

Neighborhood granules and rough rule-base in tracking

This paper presents a novel methodology for tracking a single moving object in a video sequence applying the concept of rough set theory. The novelty of this technique is that it does not consider any prior information about the video sequence unlike many existing techniques. The first target model is constructed using the median filtering based foreground detection technique and after that the target is reconstructed in every frame according to the rough set based feature reduction concept incorporating a measure of indiscernibility instead of indiscernibility matrix. The area of interest is initially defined roughly in every frame based on the object shift in the previous frames, and after reduction of redundant features the object is tracked. The measure of indiscernibility of a feature is defined based on its degree of belonging (DoB) to the target. Three quantitative indices based on rough sets, feature similarity and Bhattacharya distance are proposed to evaluate the performance of tracking and detect the mis-tracked frames in the process of tracking to make those corrected. Unlike many existing measures, the proposed ones do not require to know the ground truth or trajectory of the video sequence. Extensive experimental results are given to demonstrate the effectiveness of the method. Comparative performance is demonstrated both visually and quantitatively.

Fundamenta Informaticae

Unsupervised tracking, roughness and quantitative indices

A new spatio-temporal segmentation approach for moving object(s) detection and tracking from a video sequence is described. Spatial segmentation is carried out using rough entropy maximization, where we use the quad-tree decomposition, resulting in unequal image granulation which is closer to natural granulation. A three point estimation based on Beta Distribution is formulated for background estimation during temporal segmentation. Reconstruction and tracking of the object in the target frame is performed after combining the two segmentation outputs using its color and shift information. The algorithm is more robust to noise and gradual illumination change, because their presence is less likely to affect both its spatial and temporal segments inside the search window. The proposed methods for spatial and temporal segmentation are seen to be superior to several related methods. The accuracy of reconstruction has been significantly high. © 2012 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Granulation, rough entropy and spatiotemporal moving object detection

We present here an approach for video image segmentation where spatial segmentation is based on rough sets and granular computing and temporal segmentation is done by consecutive frame subtraction. Then the intersection of the temporal segmentation and spatial segmentation for the same frame is analyzed in RGB feature space. The estimated statistics of the intersecting regions is used for the object reconstruction and tracking. © 2011 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Spatiotemporal approach for tracking using rough entropy and frame subtraction

In this paper we propose a novel algorithm for object tracking from Video images based on segmentation and Kernel based procedure. Many Kernel based object tracking algorithms have been developed during last few years. The computational complexity becomes very high in those kernel based techniques. In our proposed method the target localization problem is minimized using segmentation technique, instead of using mean shift tracking algorithm. Following segmentation technique the localization problem of target candidate gets minimized, and then comparing the target candidate with the target model by using Bhattacharya coefficient the object can easily be detected. So, the object can be tracked with less computational burden and more efficiently. The proposed algorithm is validated with an existing video sequence and another with a real time video sequence. © 2010 IEEE.

Proceedings of the 2010 Annual IEEE India Conference: Green Energy, Computing and Communication, INDICON 2010

Real time object tracking based on segmentation and distance minimization

In this paper we propose a novel algorithm for object tracking from Video images based on segmentation and Kernel based procedure. Many Kernel based object tracking algorithms have been developed during last few years. The computational complexity becomes very high in those kernel based techniques. In our proposed method the target localization problem is minimized using segmentation technique, instead of using mean shift tracking algorithm. Following segmentation technique the localization problem of target candidate gets minimized, and then comparing the target candidate with the target model by using Bhattacharya coefficient the object can easily be detected. So, the object can be tracked with less computational burden and more efficiently. The proposed algorithm is validated with an existing video sequence and another with a real time video sequence. ©2010 IEEE.

2010 5th International Conference on Industrial and Information Systems, ICIIS 2010

Real time object tracking based on segmentation and kernel based method

In this paper we propose a novel method for object tracking in video images. The method is based on image segmentation and pattern matching. All moving and still objects in video images can be detected accurately with the help of efficient image segmentation techniques. We propose a hybrid algorithm for image segmentation using the notion of Particle Swarm Optimization (PSO) and Fuzzy-C-Means (FCM) clustering techniques. The results obtained using segmentation of successive frames are exploited for pattern matching in a simple feature space. As a consequence, multiple moving and still objects in video images are tracked simultaneously. We perform simulation experiments on object tracking to validate the efficiency of our proposed algorithm. The algorithm outperforms the existing algorithm in context of accuracy and time complexity.