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  1. AU="Bhatti, Asim"
  2. AU="Giménez Pardo, Consuelo"
  3. AU=Pollock Jennifer S AU=Pollock Jennifer S
  4. AU=Yakar Halil Ibrahim
  5. AU="O'Hara, Montana"
  6. AU="Connor, Jean A"
  7. AU="Wozniak, Kinga A"
  8. AU="Manjila, Sunil"
  9. AU="Gaviria-Cantin, Tania"
  10. AU="Pickett, Brett E"
  11. AU="Lee, Seung Yeol"
  12. AU="Waters, Aubri M"
  13. AU="Tremblay, Cyntia"
  14. AU="Sharafeldin, Tamer A"
  15. AU="Alladio, Francesca"
  16. AU="Cheng, Zhiluo"
  17. AU="Silva, Dândara Santos"
  18. AU="Timmann, Dagmar"
  19. AU="Jingping, Lin"
  20. AU="Yoon, Sangwook"
  21. AU="Sedor, John R."
  22. AU="Legrand, Julien"
  23. AU="Mintz, Kevin Todd"
  24. AU="Kösters, Markus"
  25. AU="Castano-Duque, Lina"
  26. AU="Lowry, Gregory V"
  27. AU="Gao, Xiaojuan"
  28. AU="Daniłowicz-Szymanowicz, Ludmiła"
  29. AU="Weber, Jesse N"
  30. AU="Fages-Masmiquel, Ester"
  31. AU="Macias Gil, Raul"
  32. AU="Planchat, Arnaud"
  33. AU="McElrath, Erin E"
  34. AU="Koji Ueda"
  35. AU="Pillas, Diana J"
  36. AU="Thomson, Jason J"
  37. AU="Mitra, Kalyan"
  38. AU="Sanjay Desai"
  39. AU=Cox David J AU=Cox David J
  40. AU="Grebenok, Robert J."
  41. AU="Blackburne, Brittney"
  42. AU="Bortoleti, Bruna Taciane da Silva"
  43. AU="Ehrbar, Martin"
  44. AU="Lepre, Davide"
  45. AU="Olszewska, Zuzanna"
  46. AU="Vojta, Leslie"
  47. AU=Wickstrom Eric AU=Wickstrom Eric
  48. AU="Gangavarapu, Sridevi"
  49. AU="Hussein, Hazem Abdelwaheb"
  50. AU=Cai Yixin AU=Cai Yixin
  51. AU="Hüls, Anke"
  52. AU="Poondru, Srinivasu"
  53. AU="Coca, Daniel"
  54. AU="Lebeau, Paul"
  55. AU="Dehghani, Sedigheh"
  56. AU="Ishibashi, Kenji"
  57. AU="Xu, Yanhua"
  58. AU="Matera, Katarzyna"
  59. AU="Ait-Ouarab, Slimane"
  60. AU="Nicola, Coppede"
  61. AU="Dewitt, John M"
  62. AU="Sorin M. Dudea"
  63. AU="Tanusha D. Ramdin"
  64. AU="Hao, Zehui"
  65. AU="Chauhan, Aman"

Suchergebnis

Treffer 1 - 10 von insgesamt 32

Suchoptionen

  1. Buch ; Online: Current Advancements in Stereo Vision

    Bhatti, Asim

    2012  

    Schlagwörter Computer vision ; Image processing
    Umfang 1 electronic resource (434 pages)
    Verlag IntechOpen
    Dokumenttyp Buch ; Online
    Anmerkung English ; Open Access
    HBZ-ID HT021046659
    ISBN 9789535156765 ; 9535156764
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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  2. Buch ; Online: Advances in Theory and Applications of Stereo Vision

    Bhatti, Asim

    2011  

    Schlagwörter Computer vision
    Umfang 1 electronic resource (366 pages)
    Verlag IntechOpen
    Dokumenttyp Buch ; Online
    Anmerkung English ; Open Access
    HBZ-ID HT021045569
    ISBN 9789535155027 ; 9535155024
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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  3. Buch ; Online: Stereo Vision

    Bhatti, Asim

    2008  

    Schlagwörter Robotics
    Umfang 1 electronic resource (382 pages)
    Verlag IntechOpen
    Dokumenttyp Buch ; Online
    Anmerkung English ; Open Access
    HBZ-ID HT021046697
    ISBN 9789535157717 ; 953515771X
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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  4. Artikel ; Online: Flight traits of dengue-infected Aedes aegypti mosquitoes.

    Javed, Nouman / López-Denman, Adam J / Paradkar, Prasad N / Bhatti, Asim

    Computers in biology and medicine

    2024  Band 171, Seite(n) 108178

    Abstract: Understanding the flight behaviour of dengue-infected mosquitoes can play a vital role in various contexts, including modelling disease risks and developing effective interventions against dengue. Studies on the locomotor activity of dengue-infected ... ...

    Abstract Understanding the flight behaviour of dengue-infected mosquitoes can play a vital role in various contexts, including modelling disease risks and developing effective interventions against dengue. Studies on the locomotor activity of dengue-infected mosquitoes have often faced challenges in terms of methodology. Some studies used small tubes, which impacted the natural movement of the mosquitoes, while others that used cages did not capture the three-dimensional flights, despite mosquitoes naturally flying in three dimensions. In this study, we utilised Mask RCNN (Region-based Convolutional Neural Network) along with cubic spline interpolation to comprehensively track the three-dimensional flight behaviour of dengue-infected Aedes aegypti mosquitoes. This analysis considered a number of parameters as characteristics of mosquito flight, including flight duration, number of flights, Euclidean distance, flight speed, and the volume (space) covered during flights. The accuracy achieved for mosquito detection and tracking was 98.34% for flying mosquitoes and 100% for resting mosquitoes. Notably, the interpolated data accounted for only 0.31%, underscoring the reliability of the results. Flight traits results revealed that exposure to the dengue virus significantly increases the flight duration (p-value 0.0135 × 10
    Mesh-Begriff(e) Animals ; Aedes ; Dengue Virus ; Dengue ; Reproducibility of Results ; Mosquito Vectors
    Sprache Englisch
    Erscheinungsdatum 2024-02-19
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2024.108178
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Volltext online

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  5. Artikel ; Online: Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network.

    Javed, Nouman / Paradkar, Prasad N / Bhatti, Asim

    PloS one

    2023  Band 18, Heft 7, Seite(n) e0284819

    Abstract: Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing ... ...

    Abstract Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosquito traps. However, it is difficult to understand the flight behaviour of mosquitoes due to their small sizes, complicated poses, and seemingly random moving patterns. Currently, no open-source tool is available that can detect and track resting or flying mosquitoes. Our work presented in this paper provides a detection and trajectory estimation method using the Mask RCNN algorithm and spline interpolation, which can efficiently detect mosquitoes and track their trajectories with higher accuracy. The method does not require special equipment and works excellently even with low-resolution videos. Considering the mosquito size, the proposed method's detection performance is validated using a tracker error and a custom metric that considers the mean distance between positions (estimated and ground truth), pooled standard deviation, and average accuracy. The results showed that the proposed method could successfully detect and track the flying (≈ 96% accuracy) as well as resting (100% accuracy) mosquitoes. The performance can be impacted in the case of occlusions and background clutters. Overall, this research serves as an efficient open-source tool to facilitate further examination of mosquito behavioural traits.
    Mesh-Begriff(e) Animals ; Aedes ; Algorithms ; Neural Networks, Computer ; Mosquito Vectors
    Sprache Englisch
    Erscheinungsdatum 2023-07-20
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0284819
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Physiological Synchrony Predict Task Performance and Negative Emotional State during a Three-Member Collaborative Task.

    Algumaei, Mohammed / Hettiarachchi, Imali / Veerabhadrappa, Rakesh / Bhatti, Asim

    Sensors (Basel, Switzerland)

    2023  Band 23, Heft 4

    Abstract: Evaluation of team performance in naturalistic contexts has gained popularity during the last two decades. Among other human factors, physiological synchrony has been adopted to investigate team performance and emotional state when engaged in ... ...

    Abstract Evaluation of team performance in naturalistic contexts has gained popularity during the last two decades. Among other human factors, physiological synchrony has been adopted to investigate team performance and emotional state when engaged in collaborative team tasks. A variety of methods have been reported to quantify physiological synchrony with a varying degree of correlation with the collaborative team task performance and emotional state, reflected in the inconclusive nature of findings. Little is known about the effect of the choice of synchrony calculation methods and the level of analysis on these findings. In this research work, we investigate the relationship between outcomes of different methods to quantify physiological synchrony, emotional state, and team performance of three-member teams performing a collaborative team task. The proposed research work employs dyadic-level linear (cross-correlation) and team-level non-linear (multidimensional recurrence quantification analysis) synchrony calculation measures to quantify task performance and the emotional state of the team. Our investigation indicates that the physiological synchrony estimated using multidimensional recurrence quantification analysis revealed a significant negative relationship between the subjectively reported frustration levels and overall task performance. However, no relationship was found between cross-correlation-based physiological synchrony and task performance. The proposed research highlights that the method of choice for physiological synchrony calculation has direct impact on the derived relationship of team task performance and emotional states.
    Mesh-Begriff(e) Humans ; Task Performance and Analysis ; Emotions
    Sprache Englisch
    Erscheinungsdatum 2023-02-17
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23042268
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: EggCountAI: a convolutional neural network-based software for counting of Aedes aegypti mosquito eggs.

    Javed, Nouman / López-Denman, Adam J / Paradkar, Prasad N / Bhatti, Asim

    Parasites & vectors

    2023  Band 16, Heft 1, Seite(n) 341

    Abstract: Background: Mosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the ... ...

    Abstract Background: Mosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. In existing egg-counting tools, each image requires separate processing with adjustments to various parameters such as intensity threshold and egg area size. Furthermore, accuracy decreases significantly when dealing with clustered or overlapping eggs. To overcome these issues, we have developed EggCountAI, a Mask Region-based Convolutional Neural Network (RCNN)-based free automatic egg-counting tool for Aedes aegypti mosquitoes.
    Methods: The study design involves developing EggCountAI for counting mosquito eggs and comparing its performance with two commonly employed tools-ICount and MECVision-using 10 microscopic and 10 macroscopic images of eggs laid by females on a paper strip. The results were validated through manual egg counting on the strips using ImageJ software. Two different models were trained on macroscopic and microscopic images to enhance egg detection accuracy, achieving mean average precision, mean average recall, and F1-scores of 0.92, 0.90, and 0.91 for the microscopic model, and 0.91, 0.90, and 0.90 for the macroscopic model, respectively. EggCountAI automatically counts eggs in a folder containing egg strip images, offering adaptable filtration for handling impurities of varying sizes.
    Results: The results obtained from EggCountAI highlight its remarkable performance, achieving overall accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI significantly outperformed ICount and MECVision, with ICount achieving 81.71% accuracy for micro images and 82.22% for macro images, while MECVision achieved 68.01% accuracy for micro images and 51.71% for macro images. EggCountAI also excelled in other statistical parameters, with mean absolute error of 1.90 eggs for micro, 74.30 eggs for macro, and a strong correlation and R-squared value (0.99) for both micro and macro. The superior performance of EggCountAI was most evident when handling overlapping or clustered eggs.
    Conclusion: Accurate detection and counting of mosquito eggs enables the identification of preferred egg-laying sites and facilitates optimal placement of oviposition traps, enhancing targeted vector control efforts and disease transmission prevention. In future research, the tool holds the potential to extend its application to monitor mosquito feeding preferences.
    Mesh-Begriff(e) Animals ; Female ; Humans ; Aedes ; Mosquito Vectors ; Software ; Neural Networks, Computer ; Oviposition
    Sprache Englisch
    Erscheinungsdatum 2023-10-02
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2409480-8
    ISSN 1756-3305 ; 1756-3305
    ISSN (online) 1756-3305
    ISSN 1756-3305
    DOI 10.1186/s13071-023-05956-1
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel: Advances in Understanding Vector Behavioural Traits after Infection

    Javed, Nouman / Bhatti, Asim / Paradkar, Prasad N.

    Pathogens. 2021 Oct. 24, v. 10, no. 11

    2021  

    Abstract: Vector behavioural traits, such as fitness, host-seeking, and host-feeding, are key determinants of vectorial capacity, pathogen transmission, and epidemiology of the vector-borne disease. Several studies have shown that infection with pathogens can ... ...

    Abstract Vector behavioural traits, such as fitness, host-seeking, and host-feeding, are key determinants of vectorial capacity, pathogen transmission, and epidemiology of the vector-borne disease. Several studies have shown that infection with pathogens can alter these behavioural traits of the arthropod vector. Here, we review relevant publications to assess how pathogens modulate the behaviour of mosquitoes and ticks, major vectors for human diseases. The research has shown that infection with pathogens alter the mosquito’s flight activity, mating, fecundity, host-seeking, blood-feeding, and adaptations to insecticide bed nets, and similarly modify the tick’s locomotion, questing heights, vertical and horizontal walks, tendency to overcome obstacles, and host-seeking ability. Although some of these behavioural changes may theoretically increase transmission potential of the pathogens, their effect on the disease epidemiology remains to be verified. This study will not only help in understanding virus–vector interactions but will also benefit in establishing role of these behavioural changes in improved epidemiological models and in devising new vector management strategies.
    Schlagwörter Culicidae ; disease transmission ; fecundity ; flight ; hematophagy ; host seeking ; humans ; insecticides ; locomotion ; ticks ; vector-borne diseases ; vectorial capacity
    Sprache Englisch
    Erscheinungsverlauf 2021-1024
    Erscheinungsort Multidisciplinary Digital Publishing Institute
    Dokumenttyp Artikel
    ZDB-ID 2695572-6
    ISSN 2076-0817
    ISSN 2076-0817
    DOI 10.3390/pathogens10111376
    Datenquelle NAL Katalog (AGRICOLA)

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  9. Artikel ; Online: Comparative study of encoded and alignment-based methods for virus taxonomy classification.

    Shaukat, Muhammad Arslan / Nguyen, Thanh Thi / Hsu, Edbert B / Yang, Samuel / Bhatti, Asim

    Scientific reports

    2023  Band 13, Heft 1, Seite(n) 18662

    Abstract: The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by ... ...

    Abstract The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computationally expensive and time-consuming, especially when dealing with large datasets or when detecting new virus variants is time sensitive. An alternative approach, the encoded method, has been developed that does not require prior sequence alignment and provides faster results. However, each encoded method has its own claimed accuracy. Therefore, careful evaluation and comparison of the performance of different encoded methods are essential to identify the most accurate and reliable approach for virus taxonomy classification. This study aims to address this issue by providing a comprehensive and comparative analysis of the potential of encoded methods for virus classification and phylogenetics. We compared the vectors generated for each encoded method using distance metrics to determine their similarity to alignment-based methods. The results and their validation show that K-merNV followed by CgrDft encoded methods, perform similarly to state-of-the-art multi-sequence alignment methods. This is the first study to incorporate and compare encoded methods that will facilitate future research in making more informed decisions regarding selection of a suitable method for virus taxonomy.
    Mesh-Begriff(e) Phylogeny ; Viruses/genetics ; Sequence Alignment
    Sprache Englisch
    Erscheinungsdatum 2023-10-31
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-45461-0
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: Efficient neural spike sorting using data subdivision and unification.

    Ul Hassan, Masood / Veerabhadrappa, Rakesh / Bhatti, Asim

    PloS one

    2021  Band 16, Heft 2, Seite(n) e0245589

    Abstract: Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and ... ...

    Abstract Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.
    Mesh-Begriff(e) Action Potentials ; Algorithms ; Animals ; Brain/metabolism ; Cluster Analysis ; Computational Biology/methods ; Humans ; Models, Neurological ; Neurons/metabolism ; Rats ; Signal Processing, Computer-Assisted ; Software
    Sprache Englisch
    Erscheinungsdatum 2021-02-10
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0245589
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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