Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing
    Vargas Munoz, John E. ; Srivastava, Shivangi ; Tuia, Devis ; Falcao, Alexandre X. - \ 2020
    IEEE Geoscience and Remote Sensing Magazine (2020). - ISSN 2473-2397
    Mapping of urban landuse and landcover with multiple sensors : Joining close and remote sensing with deep learning
    Srivastava, Shivangi - \ 2020
    Wageningen University. Promotor(en): D. Tuia. - Wageningen : Wageningen University - ISBN 9789463952514 - 116

    According to the Food and Agriculture Organization of the United Nations, “landuse is characterized by the arrangements, activities, and inputs by people to produce, change or maintain a certain land cover type”1. Knowledge about landuse is important to e↵ectively plan and monitor resources, infrastructure, and services in a city. This thesis is about visualizing such information in the shape of a landuse map, which can serve local governments and decision makers to plan better cities. Traditionally a field based on visual survey, landuse mapping has nowadays embraced digital technology and in particular the use of remote sensing imaging. However, it is difficult to provide a fine-grained map, at the level of the single building, using remote sensing only.

    In this thesis, I study the feasibility of using ground-based pictures for providing high- resolution land use maps. With large scale terrestrial pictures repositories pertaining to urban setting becoming available, landuse characterization maps at finer granularity seem to have higher feasibility. These pictures capture the frontal and  side  views  of urban objects and therefore can potentially lead to richer visual clues about the object. Moreover, many platforms with user uploaded content exist nowadays, such as Pixabay, Flickr, Geograph, Google Street View or Mapillary.

    But to make sense of all these images, powerful methodologies are needed. In this thesis, I explore the use of new deep learning methodologies for the task of land use mapping from multiple data points of view (the ground and the aerial). Annotations required to train these models have been sourced from online public GIS vector databases at global scale like OpenStreetMap (OSM2), or at country scale as the Dutch Kadaster. To cope with situations where such data are missing, feature extraction and semantic segmentation strategies are explored.

    The thesis is organized around four technical chapters. The first (Chapter 2) presents a method that uses several ground viewpoints of an urban object as defined in OSM, to train a model that characterizes landuse. The second (Chapter 3) explores whether top-view (aerial/satellite) imagery enhances the performance of the landuse classification model developed in Chapter 2. A multi-source (or multi-modal ) CNN model was developed over the region of Ile-de-France. It was also showed that the trained model could also be applied to another, structurally similar city (Nantes) without any  further  tuning.  In  the  third part (Chapter 4), I explore the possibility of predicting multiple land usages per building, which would lead to a more realistic map, where one  urban  object  can  be associated with several activities. The training and test of this approach were done over the city of Amsterdam. In the fourth and final part (Chapter 5), I studied model updates to multiple tasks as a way to update land maps (e.g. with building footprints) where elements are missing: I approached this problem as the one of dealing with “Catastrophic Forgetting”, a known issue that a↵ects CNNs trained for various tasks. Therefore, Chapter 5 focuses  on lifelong learning with a network pruning based approach and applies it to a challenging multi-cities dataset involving three di↵erent segmentation datasets from the DeepGlobe 2018 Challenge.

    This thesis in the end successfully explores the feasibility of automatic map generation using multiple data sources and deep learning models, therefore, opening new research op- portunities at the interface between remote sensing, GIScience and computer vision.



    Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data
    Srivastava, Shivangi ; Vargas Muñoz, John E. ; Lobry, Sylvain ; Tuia, Devis - \ 2020
    International Journal of Geographical Information Science 34 (2020)6. - ISSN 1365-8816 - p. 1117 - 1136.
    We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.
    Adaptive Compression-based Lifelong Learning
    Srivastava, S. ; Berman, M. ; Blaschko, M.B. ; Tuia, D. - \ 2019
    In: Proceedings of the British Machine Vision Conference (BMVC). - BMVA Press - 13 p.
    The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two major ways to mitigate this problem: either preserving activations of the initial network during training with a new task; or restricting the new network activations to remain close to the initial ones. The latter approach falls under the denomination of lifelong learning, where the model is updated in a way that it performs well on both old and new tasks, without having access to the old task’s training samples anymore. Recently, approaches like pruning networks for freeing network capacity during s-quential learning of tasks have been gaining in popularity. Such approaches allow learning small networks while making redundant parameters available for the next tasks. The common problem encountered with these approaches is that the pruning percentage is hard-coded, irrespective of the number of samples, of the complexity of the learning task and of the number of classes in the dataset. We propose a method based on Bayesian optimization to perform adaptive compression/pruning of the network and show its effectiveness in lifelong learning. Our method learns to perform heavy pruning for small and/or simple datasets while using milder compression rates for large and/or complex data. Experiments on classification and semantic segmentation demonstrate the applicability of learning network compression, where we are able to effectively preserve performances along sequences of tasks of varying complexity.
    Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution
    Srivastava, Shivangi ; Vargas-Muñoz, John E. ; Tuia, Devis - \ 2019
    Remote Sensing of Environment 228 (2019). - ISSN 0034-4257 - p. 129 - 143.
    Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of Île-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it suitable for automatic landuse map updates. Additionally, our approach could be easily scaled to multiple cities, because it is based on data sources available for many cities worldwide.
    Root branching toward water involves posttranslational modification of transcription factor ARF7
    Orosa-Puente, Beatriz ; Leftley, Nicola ; Wangenheim, Daniel von; Banda, Jason ; Srivastava, Anjil K. ; Hill, Kristine ; Truskina, Jekaterina ; Bhosale, Rahul ; Morris, Emily ; Srivastava, Moumita ; Kümpers, Britta ; Goh, Tatsuaki ; Fukaki, Hidehiro ; Vermeer, Joop E.M. ; Vernoux, Teva ; Dinneny, José R. ; French, Andrew P. ; Bishopp, Anthony ; Sadanandom, Ari ; Bennett, Malcolm J. - \ 2018
    Science 362 (2018)6421. - ISSN 0036-8075 - p. 1407 - 1410.

    Plants adapt to heterogeneous soil conditions by altering their root architecture. For example, roots branch when in contact with water by using the hydropatterning response. We report that hydropatterning is dependent on auxin response factor ARF7. This transcription factor induces asymmetric expression of its target gene LBD16 in lateral root founder cells. This differential expression pattern is regulated by posttranslational modification of ARF7 with the small ubiquitin-like modifier (SUMO) protein. SUMOylation negatively regulates ARF7 DNA binding activity. ARF7 SUMOylation is required to recruit the Aux/IAA (indole-3-acetic acid) repressor protein IAA3. Blocking ARF7 SUMOylation disrupts IAA3 recruitment and hydropatterning. We conclude that SUMO-dependent regulation of auxin response controls root branching pattern in response to water availability.

    Multi-label building functions classification from ground pictures using convolutional neural networks
    Srivastava, S. ; Vargas Muñoz, John E. ; Swinkels, David ; Tuia, D. - \ 2018
    In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. - New York : ACM - ISBN 9781450360364 - p. 43 - 46.
    We approach the problem of multi building function classification for buildings from the city of Amsterdam using a collection of Google Street View (GSV) pictures acquired at multiple zoom levels (field of views, FoV) and the corresponding governmental census data per building. Since buildings can have multiple usages, we cast the problem as multilabel classification task. To do so, we trained a CNN model end-to-end with the task of predicting multiple co-occurring building function classes per building. We fuse the individual features of three FoVs by using volumetric stacking. Our proposed model outperforms baseline CNN models that use either single or multiple FoVs.
    Land-use characterisation using Google Street View pictures and OpenStreetMap
    Srivastava, S. ; Lobry, Sylvain ; Tuia, D. ; Vargas Munoz, John - \ 2018
    - 5 p.
    This paper presents a study on the use of freely available, geo-referenced pictures from Google Street View to model and predict land-use at the urban-objects scale. This task is traditionally done manually and via photointerpretation, which is very time consuming. We propose to use a machine learning approach based on deep learning and to model land-use directly from both the pictures available from Google Street View and OpenStreetMap annotations. Because of the large availability of these two data sources, the proposed approach is scalable to cities around the globe and presents the possibility of frequent updates of the map. As base information, we use features extracted from single pictures around the object of interest; these features are issued from pre-trained convolutional neural networks. Then, we train various classifiers (Linear and RBF support vector machines, multi layer perceptron) and compare their performances. We report on a study over the city of Paris,France, where we observed that pictures coming from both inside and outside the urban-objects capture distinct, but complementary features.
    Deep learning based methods for building segmentation from remote sensing data
    Lobry, Sylvain ; Marcos Gonzalez, D. ; Vargas Munoz, John ; Kellenberger, B.A. ; Srivastava, S. ; Tuia, D. - \ 2018
    - 4 p.
    Does organic farming provide a viable alternative for smallholder rice farmers in India?
    Eyhorn, Frank ; Berg, Marrit van den; Decock, Charlotte ; Maat, Harro ; Srivastava, Ashish - \ 2018
    Sustainability 10 (2018)12. - ISSN 2071-1050
    Contract farming - Farming systems - Rural livelihoods - Sustainable development - System of rice intensification - Traditional varieties

    Smallholder rice farming is characterized by low returns and substantial environmental impact. Conversion to organic management and linking farmers to fair trade markets could offer an alternative. Engaging in certified cash-crop value chains could thereby provide an entry path to simultaneously reduce poverty and improve environmental sustainability. Based on comprehensive data from a representative sample of approximately 80 organic and 80 conventional farms in northern India, we compared yield and profitability of the main rotation crops over a period of five years. Contrary to the widespread belief that yields in organic farming are inevitably lower, our study shows that organic farmers achieved the same yields in cereals and pulses as conventional farmers, with considerably lower external inputs. Due to 45% lower production costs and higher sales prices, organic basmati cultivation was 105% more profitable than cultivating ordinary rice under conventional management. However, since holdings are small and the share of agricultural income of total household income is declining, conversion to organic basmati farming alone will not provide households a sufficiently attractive perspective into the future. We propose that future efforts to enhance the long-term viability of rice-based organic farming systems in this region focus on diversification involving higher value crops.

    The Sociology of Consumption in India: Towards a New Agenda
    Wessel, M.G.J. van - \ 2018
    In: Critical Themes in Indian Sociology / Srivastava, S., Arif, Y., Abraham, J., Delhi : Sage - ISBN 9789352807956 - p. 390 - 401.
    Identifying viable nutrient management interventions at the farm level : The case of smallholder organic Basmati rice production in Uttarakhand, India
    Ditzler, L. ; Breland, T.A. ; Francis, C. ; Chakraborty, M. ; Singh, D.K. ; Srivastava, A. ; Eyhorn, F. ; Groot, J.C.J. ; Six, J. ; Decock, C. - \ 2018
    Agricultural Systems 161 (2018). - ISSN 0308-521X - p. 61 - 71.
    Basmati rice - Biogas slurry - Farmyard manure - Green manure - Manure management - Nutrient management

    Smallholder farmers may gain notable livelihood benefits by participating in organic value chains. However, whether there are enough resources available to maintain organic production sustainably on smallholder farms in resource-poor regions is of concern. If not balanced by sufficient inputs, continual nutrient export via commodity crops will result in nutrient mining, and livelihood improvements gained by participating in profitable value chains could be negated by soil degradation in the long term. The objectives of this study were to test an integrated approach for understanding the farm-level impacts of subsystem nutrient management actions and to identify locally viable interventions for increased nutrient supply and recycling. We employ a systems analysis methodology to address the nutrient gaps on smallholder farms in Uttarakhand, India producing organic Basmati rice for an international value chain. Farmers here rely on few livestock (three to five head of cattle ha− 1) to supply nutrient inputs and are achieving smaller than potential Basmati yields. We surveyed 42 small farms (< 3.5 ha, average annual income around $1000 year− 1) and analyzed available manure stocks for nutrient contents in order to trace the farm-level flow of manure nutrients, identify vectors of avoidable nutrient loss, and systematically identify locally relevant and feasible improvements. The interventions identified as viable were reducing nutrient losses through simple and relatively cheap manure management modifications (i.e. using straw bedding to capture livestock urine, covering farmyard manure stockpiles with plastic sheeting, enclosed biogas slurry storage, and using biogas slurry for improved compost production), in situ green manuring, and purchasing farmyard manure. Cost–benefit analyses predicted that proposed interventions could increase farmers’ net profit by up to 40% while also addressing problematic nutrient gaps. While our results pertain specifically to Uttarakhand, we found that our integrated research approach worked well to address the problem of nutrient gaps on resource-poor smallholder organic farms, and believe that the strategy could be used with equal success to address similar problems in other regions.

    Joint height estimation and semantic labeling of monocular aerial images with CNNS
    Srivastava, Shivangi ; Volpi, Michele ; Tuia, Devis - \ 2017
    In: 2017 IEEE International Geoscience and Remote Sensing Symposium. - Institute of Electrical and Electronics Engineers Inc. (International Geoscience and Remote Sensing Symposium (IGARSS) ) - ISBN 9781509049523 - p. 5173 - 5176.
    Convolutional neural networks - Digital Surface Model - Multitask learning - Semantic labeling

    We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and another predicting normalized Digital Surface Model (nDSM) from the pixel values. Since the nDSM/height information is used only in the second loss, there is no need to have a nDSM map at test time, and the model can estimate height automatically on new images. We test our proposed method on a set of sub-decimeter resolution images and show that our model equals the performances of two separate models, but at the cost of a single one.

    Managing manure for sustainable organic Basmati rice production
    Ditzler, L. ; Breland, T.A. ; Francis, C. ; Chakraborty, M. ; Singh, D.K. ; Srivastava, A. ; Eyhorn, F. ; Groot, J.C.J. ; Six, J. ; Decock, C. - \ 2016
    How to intensify organic Basmati production in Uttarakhand, India?
    Decock, C. ; Chakraborty, M. ; Singh, D.K. ; Srivastava, A. ; Eyhorn, F. ; Ditzler, L. ; Groot, J.C.J. ; Six, J. - \ 2016
    Biosensor-based detection of tuberculosis
    Srivastava, Saurabh K. ; Rijn, Cees J.M. Van; Jongsma, Maarten A. - \ 2016
    RSC Advances : An international journal to further the chemical sciences 6 (2016)22. - ISSN 2046-2069 - p. 17759 - 17771.

    Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb.), is one of the most prevalent and serious infectious diseases worldwide with an estimated annual global mortality of 1.4 million in 2010. Diagnosis of TB in the developing world is very challenging due to the limited suitability of currently available techniques under tropical field conditions. M. tb. is a slowly growing Mycobacterium that takes around six to eight weeks to be detected via sensitive culture methods. There is also hardly any clinical symptom at an early stage of infection, thereby causing a delay in diagnosis and treatment, and the complexity of the disease is further increased by the emergence of multiple drug resistant (MDR) strains. A lot of work has been done over the last few decades to develop effective point of care diagnostic techniques that are cheap, robust and can be performed at high throughput in rural areas. However, despite considerable technical improvements reported from the lab, such economical fool-proof diagnostic assays are still lacking on the market. The objective of this review is to evaluate currently available biosensing techniques that are either already in use or under development for detection of TB. The focus of the review is on the emerging field of diagnostic biosensors that combine ligand capture and detection in a one-step assay. A comparison will also be made with conventional multistep techniques.

    A generic microfluidic biosensor of G protein-coupled receptor activation - impedance measurements of reversible morphological changes of reverse transfected HEK293 cells on microelectrodes
    Srivastava, S.K. ; Ramaneti, R. ; Roelse, M. ; Duy Tong, H. ; Vrouwe, E.X. ; Brinkman, A.G.M. ; Smet, L.C.P.M. de; Rijn, C.J.M. van; Jongsma, M.A. - \ 2015
    RSC Advances : An international journal to further the chemical sciences 5 (2015). - ISSN 2046-2069 - p. 52563 - 52570.
    drug discovery - assays - technology - mechanism - responses - targets - design
    Impedance spectroscopy of cell lines on interdigitated electrodes (IDEs) is an established method of monitoring receptor-specific cell shape changes in response to certain analytes. Normally, assays are done in multiwells making it a bulky, static and single use procedure. Here, we present a biosensor allowing sequential application of biological test samples with an automated microfluidic system. It is capable of monitoring relative changes in impedance using castellated IDEs of 250–500 mm diameter, covered with stable or reverse transfected HEK293 cells. Reversible activation of the Neurokinin 1 (NK1) receptor in stable cell lines was observed in response to a series of 5 minute exposures from 1 pM–10 nM of the specific ligand Substance P (SP) using impedance measurements at 10 mV and 15 kHz. An optimal flow speed of 10 ml min 1 was chosen for the 10 ml flow cell. The EC50 of 10 pM was about 10 times lower than the EC50 based on measuring changes in the calcium ion concentration. The method was also shown to work with reverse transfected cells. Plasmid DNA encoding the NK1 gene was spotted onto the electrodes and pre-incubated with a transfection agent. The overlaid HEK293 cells were subsequently transfected by the underlying DNA. After challenge with SP, the cells induced an activation response similar to the stable cell line. The microfluidic micro-electrode reverse transfection system opens up possibilities to perform parallel measurements on IDE arrays with distinct receptors per IDE in a single flow channel .
    Biosensor based detection of tuberculosis biomarkers
    Srivastava, S.K. - \ 2014
    Wageningen University. Promotor(en): Cees van Rijn, co-promotor(en): Maarten Jongsma. - Wageningen : Wageningen University - ISBN 9789462571587 - 145
    tuberculose - diagnostische technieken - immunologische technieken - bionanotechnologie - biosensoren - heat shock eiwitten - mycobacterium tuberculosis - tuberculosis - diagnostic techniques - immunological techniques - bionanotechnology - biosensors - heat shock proteins - mycobacterium tuberculosis
    16 kDa Heat Shock Protein from Heat-Inactivated Mycobacterium tuberculosis Is a Homodimer – Suitability for Diagnostic Applications with Specific Llama VHH Monoclonals
    Srivastava, S.K. ; Ruigrok, V.J.B. ; Thompson, N.J. ; Trilling, A.K. ; Heck, A.J.R. ; Rijn, C.J.M. van; Beekwilder, M.J. ; Jongsma, M.A. - \ 2013
    PLoS ONE 8 (2013)5. - ISSN 1932-6203
    hiv-associated tuberculosis - immunological diagnosis - antibody fragments - mass-spectrometry - skin-test - antigen - complexes - responses - peptides - epitopes
    Background: The 16 kDa heat shock protein (HSP) is an immuno-dominant antigen, used in diagnosis of infectious Mycobacterium tuberculosis (M.tb.) causing tuberculosis (TB). Its use in serum-based diagnostics is limited, but for the direct identification of M.tb. bacteria in sputum or cultures it may represent a useful tool. Recently, a broad set of twelve 16 kDa specific heavy chain llama antibodies (VHH) has been isolated, and their utility for diagnostic applications was explored. Methodology/Principal Findings: To identify the epitopes recognized by the nine (randomly selected from a set of twelve 16 kDa specific VHH antibodies) distinct VHH antibodies, 14 overlapping linear epitopes (each 20 amino acid long) were characterized using direct and sandwich ELISA techniques. Seven out of 14 epitopes were recognized by 8 out of 9 VHH antibodies. The two highest affinity binders B-F10 and A-23 were found to bind distinct epitopes. Sandwich ELISA and SPR experiments showed that only B-F10 was suitable as secondary antibody with both B-F10 and A-23 as anchoring antibodies. To explain this behavior, the epitopes were matched to the putative 3D structure model. Electrospray ionization time-of-flight mass spectrometry and size exclusion chromatography were used to determine the higher order conformation. A homodimer model best explained the differential immunological reactivity of A-23 and B-F10 against heat-treated M.tb. lysates. Conclusions/Significance: The concentrations of secreted antigens of M.tb. in sputum are too low for immunological detection and existing kits are only used for identifying M.tb. in cultures. Here we describe how specific combinations of VHH domains could be used to detect the intracellular HSP antigen. Linked to methods of pre-concentrating M.tb. cells prior to lysis, HSP detection may enable the development of protein-based diagnostics of sputum samples and earlier diagnosis of diseases.
    A Broad Set of Different Llama Antibodies Specific for a 16kDa Heat Shock Protein of Mycobacterium tuberculosis
    Trilling, A.K. ; Ronde, H. de; Noteboom, L. ; Houwelingen, A.M.M.L. van; Roelse, M. ; Srivastava, S.K. ; Haasnoot, W. ; Jongsma, M.A. ; Kolk, A. ; Zuilhof, H. ; Beekwilder, J. - \ 2011
    PLoS ONE 6 (2011)10. - ISSN 1932-6203 - 10 p.
    b-cell - monoclonal-antibodies - rapid detection - in-vitro - antigen - single - fragments - resistance - epitopes - binding
    Background Recombinant antibodies are powerful tools in engineering of novel diagnostics. Due to the small size and stable nature of llama antibody domains selected antibodies can serve as a detection reagent in multiplexed and sensitive assays for M. tuberculosis. Methodology/Principal Findings Antibodies for Mycobacterium tuberculosis (M. tb) recognition were raised in Alpaca, and, by phage display, recombinant variable domains of heavy-chain antibodies (VHH) binding to M. tuberculosis antigens were isolated. Two phage display selection strategies were followed: one direct selection using semi-purified protein antigen, and a depletion strategy with lysates, aiming to avoid cross-reaction to other mycobacteria. Both panning methods selected a set of binders with widely differing complementarity determining regions. Selected recombinant VHHs were produced in E. coli and shown to bind immobilized lysate in direct Enzymelinked Immunosorbent Assay (ELISA) tests and soluble antigen by surface plasmon resonance (SPR) analysis. All tested VHHs were specific for tuberculosis-causing mycobacteria (M. tuberculosis, M. bovis) and exclusively recognized an immunodominant 16 kDa heat shock protein (hsp). The highest affinity VHH had a dissociation constant (KD) of 4×10-10 M. Conclusions/Significance A broad set of different llama antibodies specific for 16 kDa heat shock protein of M. tuberculosis is available. This protein is highly stable and abundant in M. tuberculosis. The VHH that detect this protein are applied in a robust SPR sensor for identification of tuberculosis-causing mycobacteria.
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