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  • (2022) Zhang, Qi
    Thesis
    As a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing.

  • (2022) Zhao, Runqing
    Thesis
    Emerging modes of air transport such as autonomous airport shuttle and air taxi are potentially efficient alternatives to current transport practices such as bus and train. This thesis examines bus shuttle service within an airport and air metro as two examples of network design. Within an airport, the bus shuttle serves passengers between the terminals, train stations, parking lots, hotels, and shopping areas. Air metro is a type of pre-planned service in urban air mobility that accommodates passengers for intra- or inter-city trips. The problems are to optimise the service, and the outputs including the optimal fleet size, dispatch pattern and schedule. Based on the proposed time-space networks, the service network design problems are formulated as mixed integer linear programs. The heterogeneous multi-type bus fleet case and stochastic demand case are extended for the airport shuttle case, while a rolling horizon optimisation is adopted for the air metro case. In the autonomous airport inter-terminal bus shuttle case, a Monte Carlo simulation-based approach is proposed to solve the case with demand stochasticity, which is then further embedded into an "effective" passenger demand framework. The "effective" demand is the summation of mean demand value and a safety margin. By comparing the proposed airport shuttle service to the current one, it is found that the proposed service can save approximately 27% of the total system cost. The results for stochastic problem suggest estimating the safety margin to be 0.3675 times of the standard deviation brings the best performance. For the second case, the service network design is extended with a pilot scheduling layer and simulation is undertaken to compare the autonomous (pilot-less) and piloted service design. The results suggest that an autonomous air metro service would be preferable if the price of an autonomous aircraft is less than 1.6 times the price of a human-driven one. The results for rolling horizon optimisation suggest to confirm the actual demand at least 45 minutes prior to departure. Based on data from the Sydney (Australia) region, the thesis provides information directly relevant for the service network design of emerging modes of air transport in the city.

  • (2021) Alohali, Ruaa Tawfiq A
    Thesis
    The Arabian basin was subject to several tectonic events, including Lower Cambrian Najd rifting, the Carboniferous Hercynian Orogeny, Triassic Zagros rifting, and the Early/Cretaceous and Late/Tertiary Alpine orogenic events. These events reactivated Precambrian basement structures and affected the structural configuration of the overlying Paleozoic cover succession. In addition to a 2D seismic array and several drill well logs, a newly acquired, processed 3D seismic image of the subsurface in part of the basin covering an area of approximately 1051 km2 has been provided to improve the understanding of the regional tectonic evolution associated with these deformation events. In this study, a manual interpretation is presented of six main horizons from the Late Ordovician to the Middle Triassic. Faults and folds were also mapped to further constrain the stratigraphic and structural framework. Collectively, this data is used to build a geological model of the region and develop a timeline of geological events. Results show that a lower Paleozoic sedimentary succession between the Late Silurian to the Early Permian was subject to localised tilting, uplift, and erosion during the Carboniferous Hercynian Orogeny, forming a regional unconformity. Subsequent deposition occurred from the Paleozoic to the Mesozoic, producing a relatively thick, conformable, upper succession. The juxtaposition of the Silurian rocks and Permian formations allows a direct fluid flow between the two intervals. Seismic analysis also indicated two major fault generations. A younger NNW-striking fault set with a component of reverse, east-side-up displacement affected the Lower Triassic succession and is most likely related to the Cretaceous and Tertiary Alpine Events that reactivated the Najd fault system. These fault structures allow vertical migration that could act as conduits to form structural traps. Manual mapping of fault structures in the study area required significant time and effort. To simplify and accelerate the manual faults interpretation in the study area, a fault segmentation method was developed using a Convolutional Neural Network. This method was implemented using the 3D seismic data acquired from the Arabian Basin. The network was trained, validated, and tested with samples that included a seismic cube and fault images that were labelled manually corresponding to the seismic cube. The model successfully identified faults with an accuracy of 96% and an error rate of 0.12 on the training dataset. To achieve a more robust model, the prediction results were further enhanced using postprocessing by linking discontinued segments of the same fault and thus, reducing the number of detected faults. This method improved the accuracy of the prediction results of the proposed model using the test dataset by 77.5%. Additionally, an efficient framework was introduced to correlate the predictions and the ground truth by measuring their average distance value. This technique was also applied to the F3 Netherlands survey, which showed promising results in another region with complex fault geometries. As a result of the automated technique developed here, fault detection and diagnosis were achieved efficiently with structures similar to the trained dataset and has a huge potential in improving exploration targets that are structurally controlled by faults.

  • (2021) Ly, Kongmeng
    Thesis
    The management of transboundary river basins across developing countries, such as the Lower Mekong River Basin (LMB), is frequently challenging given the development and conservation divergences of the basin countries. Driven by needs to sustain economic performance and reduce poverty, the LMB countries are embarking on significant land use changes in the form hydropower dams, to fulfill their energy requirements. This pathway could lead to irreversible changes to the ecosystem of the Mekong River, if not properly managed. This thesis aims to explore the potential effects of changes in land use —with a focus on current and projected hydropower operations— on the Lower Mekong River network streamflow and instream water quality. To achieve this aim, this thesis first examined the relationships between the basin land use/land cover attributes, and streamflow and instream water quality dynamics of the Mekong River, using total suspended solids and nitrate as proxies for water quality. Findings from this allowed framing challenges of integrated water management of transboundary river basins. These were used as criteria for selecting eWater’s Source modelling framework as a management tool that can support decision-making in the socio-ecological context of the LMB. Against a combination of predictive performance metrics and hydrologic signatures, the model’s application in the LMB was found to robustly simulate streamflow, TSS and nitrate time series. The model was then used for analysing four plausible future hydropower development scenarios, under extreme climate conditions and operational alternatives. This revealed that hydropower operations on either tributary or mainstream could result in annual and wet season flow reduction while increasing dry season flows compared to a baseline scenario. Conversely, hydropower operation on both tributary and mainstream could result in dry season flow reduction. Both instream TSS and nitrate loads were predicted to reduce under all three scenarios compared to the baseline. These effects were found to magnify under extreme climate conditions, but were less severe under improved operational alternatives. In the LMB where hydropower development is inevitable, findings from this thesis provide an enhanced understanding on the importance of operational alternatives as an effective transboundary cooperation and management pathway for balancing electricity generation and protection of riverine ecology, water and food security, and people livelihoods.

  • (2022) Shahriari, Siroos
    Thesis
    Time series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims: (1) To combine parametric and bootstrapping techniques within time series models; (2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and (3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data. The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types.

  • (2022) Kaur, Sandeep
    Thesis
    Advances in molecular biology data collection, leading to the accumulation of large amounts of diverse data, call for novel computational approaches to enable their effective analysis. This thesis explored the application of visual-analytics-driven bioinformatics approaches to four biomolecular data-driven challenges. For analysing time-series omic and multiomic data, a novel method, Minardo-Model, was developed. Minardo-Model can identify key events (e.g. phosphorylation) from such time-series data and temporally order them. To visualise the inferred order of events, two novel visualisation approaches, event maps and event sparklines, were developed. Minardo-Model was tested using two time-series datasets and in both cases, the event orderings derived by this method correlated with prior knowledge. To streamline the use of experimental 3D protein structures for analysing sequence variants, a novel method was developed and integrated into Aquaria. For variants specified in the HGVS notation, the method identifies and displays a best matching structure. Additionally, for each variant specified, all structures spanning the variant, and containing the exact variant (missense only), along with sequence features retrieved from external resources, are summarised. The developed approach was used to analyse variants in human ACE2, and SARS-CoV-2 spike, revealing novel insights. For pathogenic bacterial isolates characterised using multilevel genome typing (MGT), the MGTdb web service was developed. MGTdb, enables upload of isolates as sequence reads or extracted alleles, which are processed and assigned the MGT-identifiers. The features of MGTdb, such as interactive visualisation tools, data download and export to external software, enable epidemiological exploration in the context of the local or global database of isolates. The usability of MGTdb was successfully demonstrated through three case studies. For identifying insertion sequences (IS) from short-read sequencing data, a novel method, WiIS, was developed. WiIS was tested on Bordetella pertussis isolates, for which both short-read (test data) and long-read sequences (ground truth) were available - WiIS was found to have high precision and recall. It also outperformed other published tools in identifying IS in B. pertussis genomes. The novel bioinformatics methods developed in this thesis enable novel analysis of a wide variety of data thus providing insight into various biomolecular processes.

  • (2022) Nguyen, Minh Triet
    Thesis
    Singlet fission is a photo-physical process that generates two triplet excitons from one singlet exciton and can potentially enhance efficiency in photovoltaic systems. The combination of photovoltaics and singlet fission is a novel field for solar energy conversion when there is much interest in renewable, non-destructive, and continuously available energy sources. Singlet fission can also overcome thermalization losses in photovoltaics, which happens in traditional cells when the incident photon energy is higher than the silicon bandgap energy, using a carrier multiplication mechanism. This thesis will design, construct, and characterize photovoltaic devices incorporating singlet fission materials to study singlet fission in practical application. The research focuses on materials characterization, spin dynamics, and electron transfers between acene and the semiconductor layer in Au/TiO2 ballistic cells, and the incorporation of singlet fission layers on silicon-based cell structures. In detail, a set of investigations was developed and summarized by implementing singlet fission materials into a state-of-the-art ballistic photovoltaic device and silicon-based solar cell. The studies demonstrate proof of concept and rationally explain the process. The first part of the thesis investigates thin films of pentacene, TIPS-pentacene, and tetracene via crystallinity, morphology, absorption, and thickness characterization. Additionally, Au and TiO2 layers in Schottky device structures were optimized to achieve the best performance for energy transfer from an applied dye layer (merbromin). The drop-casted dye layer influences the device performance by increasing short-circuit current and open-circuit voltage, demonstrating the ability of charge transfer between the device and the applied film. This device structure provides a test bed for studying charge and energy transfer from singlet fission films. The latter part of the thesis describes several investigations to understand singlet fission in a thin film using this architecture. Magneto-photoconductivity measurements were primarily used to observe the spin dynamics via photoconductivity under an external magnetic field. Control experiments with bare Au/TiO2 devices showed no observable magneto-photoconductivity signal. In contrast, devices with pentacene and tetracene singlet fission layers showed a strong magnetoconductivity effect caused by ballistic electron transfer from the singlet fission layer into the TiO2 n-type semiconductor through an ultra-thin gold layer inserted between the layers. A qualitatively different behavior is seen between the pentacene and tetracene, which reveals that the energy alignment plays a crucial part in the charge transfer between the singlet fission layer and the device. The last section investigates the application of pentacene and tetracene evaporated thin-films as sensitizer layers to a silicon-based solar cell. The optimized Si cell structure with the annealing treatment improved the cell's performance by increasing short-circuit current and open-circuit voltage. The deposition of pentacene and tetracene as sensitizer layers into the device showed some results but posed several challenges that need to be addressed. As the current-voltage and external quantum efficiency measurements were taken, it was observed that material interfaces need to be designed to fully achieve the singlet fission of the acene layer into the Si devices.

  • (2023) Broadbent, Gail
    Thesis
    To obviate significant and growing road vehicle greenhouse gas (GHG) emissions contributing to climate change, transitioning to battery electric vehicles (BEV) is urgently required to maximise fleet emissions reductions soonest, deploying the most suitable available technology. Many countries have implemented policies to incentivise electric vehicle (EV) uptake, which have been well studied. This thesis undertakes novel research by employing a case study of New Zealand to examine consumer responses to EV policies implemented in 2016, plus two mooted policies. Questionnaires and interviews surveyed private motorists from a demand perspective, capturing quantitative and qualitative data to assess attitudes, values, and perceptions of EVs, awareness of government policies, and to reveal those most popular. Employing a unique innovation, four motorist groups (segmented by attitude to EVs, which influences adoption rates) were compared. As additional novelty the role of communication channels, including print media, in influencing consumer behaviour was investigated. Results revealed New Zealand’s conventional motorists, in contrast with EV owners, had low policy awareness, confirming international findings. EV Positives, the next-most ‘EV ready’ segment, favoured policies designed to reduce EV purchase price and increase nationwide charger deployment. Concordant with social marketing research, governments should focus on such buyers’ preferences. Furthermore, to improve BEV readiness, disseminating updated information about EVs via multiple communication channels could shift perceptions of EVs from ‘expensive and inconvenient’ to ‘fun and economical’. Thus, two key concepts namely purchase price-parity and charging infrastructure availability, were incorporated into models specifically for Australia, where policies are limited, to investigate the feasibility of transitioning Australia’s road vehicle fleet to electromobility to achieve net-zero emissions by 2050. A national scale, integrated, macro-economic, system dynamics model (iSDG Australia) was used innovatively to project Australia’s future road transport demand, vehicle mix, energy consumption and GHG emissions. Firstly, the model applied numerous ‘adoption target’ scenarios comparing them to Business-as-Usual; secondly, various combinations of policy options were modelled to project potential outcomes and implementation costs. Based on the assumptions, results suggest emissions reductions are maximised by the fastest passenger vehicle fleet transition to BEVs, entailing declining but ongoing transformational government policy support to achieve net-zero by 2050.

  • (2023) Zillur Rahman, Kazi Mohammad
    Thesis
    Current healthcare infection surveillance rarely monitors the distribution of antimicrobial resistance (AMR) in bacteria beyond clinical settings in Australia and overseas. This results in a significant gap in our ability to fully understand and manage the spread of AMR in the general community. This thesis explores whether wastewater-based monitoring could reveal geospatial-temporal and demographic trends of antibiotic-resistant bacteria in the urban area of Greater Sydney, Australia. Untreated wastewater from 25 wastewater treatment plants sampled between 2017 and 2019 consistently contained extended-spectrum β-lactamases-producing Enterobacteriaceae (ESBL-E) isolates, suggesting its endemicity in the community. Carbapenem-resistant Enterobacteriaceae (CRE), vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) isolates were occasionally detected. Demographic and healthcare infection-related factors correlated with the ESBL-E load, and demographic variables influenced the VRE load. In contrast, the healthcare infection-related factor mainly drove the CRE load. These findings demonstrate the potential of wastewater-based surveillance to understand the factors driving AMR distribution in the community. The subsequent thesis work covers the genomic characterisation of selected ESBL-E and CRE wastewater isolates to reveal their nature, origin, and underlying resistance mechanisms. Phylogenetic analysis showed that Escherichia coli isolates were related to high-risk human-associated pandemic clones and non-human-associated clones. The Klebsiella pneumoniae and K. variicola isolates were related to globally disseminated and emerging human-associated clones, and some were detected for the first time in Australia. Genomic analysis also indicated novel resistance mechanisms against nitrofurantoin in E. coli, and against piperacillin/tazobactam and ticarcillin/clavulanic acid in Klebsiella isolates. The virulence gene content indicated that some E. coli and Klebsiella isolates were likely associated with infections, while the asymptomatic carriage was suggested for other isolates. These results demonstrate a clear potential for wastewater-based surveillance to monitor the emergence and dissemination of resistance in non-clinical isolates, and in particular, isolates from the community and non-human sources. The findings of this study can complement healthcare infection surveillance to inform management strategies to mitigate the emergence and dissemination of AMR and important human pathogens in the general community.

  • (2022) Melodia, Daniele
    Thesis
    Antibodies are increasingly useful therapeutics, and examples include the checkpoint inhibitors pembrolizumab1 and ipilimumab2 in cancer immunotherapy, and anti tau therapies in Alzheimer’s disease and dementia.3,4 However, specific applications requiring cytosolic delivery of the antibody, or transport across the blood-brain barrier pose challenges to antibody therapeutics. These issues may reduce the effectiveness of immunotherapy and restrict it to extracellular targets. Conjugating polymers to proteins and enzymes has been very effective at improving their stability and pharmacokinetics,5–8 and similar approaches have been studied for antibody conjugation.9–12 Finding an effective polymeric delivery system for antibodies can greatly improve immunotherapy. In this work three strategies were explored for the encapsulation and bioconjugation to antibodies. The first approach is the encapsulation via electrostatic interactions between the antibody and a charged block copolymer to form polyion complex (PIC) micelles. Polyphosphonium block copolymers were studied for the first time to encapsulate antibodies, and were compared to their ammonium counterpart. While this approach has the advantage of being reversible, the polymer-antibody electrostatic interactions were too weak for biological applications, and delivery by this means would require a crosslinking strategy. The second approach involves covalent attachment of polymers on the antibody’s surface via a grafting from polymerisation. An oxygen tolerant technique was employed for the screening of a large number of samples in low volumes (<100 μL). Successful grafting was demonstrated by AF4 and gel electrophoresis. Enzyme-linked immunosorbent assay (ELISA) showed retention of up to 40% binding activity relative to the native antibody with a marked improvement in stability. The third strategy introduces a novel acid sensitive linker for the reversible covalent attachment of polymers to the antibody’s surface. This was achieved by using Diels-Alder chemistry to create an activated PEG that forms an amide with a conformational lock similar to citraconic anhydride upon conjugation to the amines on the antibody. The ability of the linker to cleave at pH 5.5 is demonstrated, resulting in almost complete recovery of the original binding activity of the antibody. Overall, the reversible covalent attachment investigated here seems the most promising, and combining the high throughput method with the cleavable linker approach holds great potential for advancing in immunotherapy. References (1) Reck, M. Pembrolizumab as First-Line Therapy for Metastatic Non-Small-Cell Lung Cancer. Futur. Med. 2018, 10, 93–105. (2) Gao, J.; Ward, J. F.; Pettaway, C. A.; Shi, L. Z.; Subudhi, S. K.; Vence, L. M.; Zhao, H.; Chen, J.; Chen, H.; Efstathiou, E.; Troncoso, P.; Allison, J. P.; Logothetis, C. J.; Wistuba, I. I.; Sepulveda, M. A.; Sun, J.; Wargo, J.; Blando, J. VISTA Is an Inhibitory Immune Checkpoint That Is Increased after Ipilimumab Therapy in Patients with Prostate Cancer. Nat. Med. 2017, 23 (5), 551–555.. (3) Pedersen, J. T.; Sigurdsson, E. M. Tau Immunotherapy for Alzheimer’s Disease. Trends Mol. Med. 2015, 21 (6), 394–402. (4) Castillo-Carranza, D. L.; Sengupta, U.; Guerrero-Munoz, M. J.; Lasagna-Reeves, C. A.; Gerson, J. E.; Singh, G.; Estes, D. M.; Barrett, A. D. T.; Dineley, K. T.; Jackson, G. R.; Kayed, R. Passive Immunization with Tau Oligomer Monoclonal Antibody Reverses Tauopathy Phenotypes without Affecting Hyperphosphorylated Neurofibrillary Tangles. J. Neurosci. 2014, 34 (12), 4260–4272. (5) Abolmaali, S. S.; Tamaddon, A. M.; Salmanpour, M.; Mohammadi, S.; Dinarvand, R. Block Ionomer Micellar Nanoparticles from Double Hydrophilic Copolymers, Classifications and Promises for Delivery of Cancer Chemotherapeutics. Eur. J. Pharm. Sci. 2017, 104 (January), 393–405. (6) Kurakhmaeva, K. B.; Djindjikhashvili, I. A.; Petrov, V. E.; Balabanyan, V. U.; Voronina, T. A.; Trofimov, S. S.; Kreuter, J.; Gelperina, S.; Begley, D.; Alyautdin, R. N. Brain Targeting of Nerve Growth Factor Using Poly(Butyl Cyanoacrylate) Nanoparticles. J. Drug Target. 2009, 17 (8), 564–574. (7) Jiang, Y.; Fay, J. M.; Poon, C. D.; Vinod, N.; Zhao, Y.; Bullock, K.; Qin, S.; Manickam, D. S.; Yi, X.; Banks, W. A.; Kabanov, A. V. Nanoformulation of Brain-Derived Neurotrophic Factor with Target Receptor-Triggered-Release in the Central Nervous System. Adv. Funct. Mater. 2017, 1703982, 1–11. (8) Klyachko, N. L.; Manickam, D. S.; Brynskikh, A. M.; Uglanova, S. V.; Li, S.; Higginbotham, S. M.; Bronich, T. K.; Batrakova, E. V.; Kabanov, A. V. Cross-Linked Antioxidant Nanozymes for Improved Delivery to CNS. Nanomedicine Nanotechnology, Biol. Med. 2012, 8 (1), 119–129. (9) Bin Liu, Khushboo Singh , Shuai Gong , Mine Canakci, Barbara A. Osborne, and S. T. Protein Antibody Conjugates PACs A Plug‐and‐Play Strategy for Covalent Conjugation and Targeted Intracellular Delivery of Pristine Proteins. Angew. Chemie 2021, 133, 12923–12928. (10) Chan, L. J.; Bulitta, J. B.; Ascher, D. B.; Haynes, J. M.; Mcleod, V. M.; Porter, C. J. H.; Williams, C. C.; Kaminskas, L. M. PEGylation Does Not Signi Fi Cantly Change the Initial Intravenous or Subcutaneous Pharmacokinetics or Lymphatic Exposure of Trastuzumab in Rats but Increases Plasma Clearance after Subcutaneous Administration. Mol. Pharm. 2015, 12, 794–809. (11) Subasic, C. N.; Ardana, A.; Chan, L. J.; Huang, F.; Scoble, J. A.; Butcher, N. J.; Meagher, L.; Chiefari, J.; Kaminskas, L. M.; Williams, C. Poly ( HPMA- Co -NIPAM ) Copolymer as an Alternative to Polyethylene Glycol-Based Pharmacokinetic Modulation of Therapeutic Proteins. Int. J. Pharm. 2021, 608 (September), 121075. (12) Keita Hironaka,a,b Erika Yoshihara, Ahmed Nabil, James J. Lai, A. K. and M. E. Conjugation of Antibody with Temperature-Responsive Polymer via in Situ Click Reaction to Enable Biomarker Enrichment for Increased Diagnostic Sensitivity. Biomater. Sci. 2021, 9, 4870–4879.