1,159 research outputs found
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Geoarchaeological Approaches to Pictish Settlement Sites: Assessing Heritage at Risk
Due to the poor preservation of Pictish period buildings and the occupation deposits within them, very little is known of daily life in early medieval Scotland. In lowland and coastal areas, Pictish buildings are generally truncated by deep ploughing, coastal erosion, or urban development, while those uncovered in upland areas seem to have no preserved floor deposits for reasons that remain poorly understood. Geoarchaeological techniques are particularly effective in clarifying site formation processes and understanding post-depositional transformations. They are also a powerful research tool for identifying floor deposits, distinguishing their composition, and linking this to daily activities. However, archaeologists are often reluctant to apply geoarchaeological methods if they suspect preservation is poor or stratigraphy is not visible in the field.
This study therefore employs an innovative suite of geoarchaeological techniques to evaluate the preservation of Pictish period buildings and the potential that fragmentary buildings have to reconstruct daily life in early medieval Scotland. Alongside literature analysis and a desk-based comparison with national soil datasets, over 400 sediment samples from three key settlement sites were subjected to integrated soil micromorphology, x-ray fluorescence, magnetic susceptibility, loss-on-ignition, pH, electrical conductivity and microrefuse analysis. The combined data were successful in generating new information about the depositional and post-depositional history of the sites, preservation conditions of the occupation deposits, and activity areas within domestic dwellings. Most significantly, the integrated approach demonstrated that ephemeral and fragmented occupation surfaces retain surviving characteristics of the use of space, even if floors are not preserved well enough to be clearly defined in the field or in thin-section. A partnership with Historic Environment Scotland has channelled this work into research-led guidelines aimed at communicating geoarchaeological methods and principles to a wider audience
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Towards Arginase Inhibition: Hybrid SAR Protocol for Property Mapping of Chlorinated N-arylcinnamamides
peer reviewedA series of seventeen 4-chlorocinnamanilides and seventeen 3,4-dichlorocinnamanilides were characterized for their antiplasmodial activity. In vitro screening on a chloroquine-sensitive strain of Plasmodium falciparum 3D7/MRA-102 highlighted that 23 compounds possessed IC50 < 30 µM. Typically, 3,4-dichlorocinnamanilides showed a broader range of activity compared to 4-chlorocinnamanilides. (2E)-N-[3,5-bis(trifluoromethyl)phenyl]-3-(3,4-dichlorophenyl)prop-2-en-amide with IC50 = 1.6 µM was the most effective agent, while the other eight most active derivatives showed IC50 in the range from 1.8 to 4.6 µM. A good correlation between the experimental logk and the estimated clogP was recorded for the whole ensemble of the lipophilicity generators. Moreover, the SAR-mediated similarity assessment of the novel (di)chlorinated N-arylcinnamamides was conducted using the collaborative (hybrid) ligand-based and structure-related protocols. In consequence, an ‘averaged’ selection-driven interaction pattern was produced based in namely ‘pseudo–consensus’ 3D pharmacophore mapping. The molecular docking approach was engaged for the most potent antiplasmodial agents in order to gain an insight into the arginase-inhibitor binding mode. The docking study revealed that (di)chlorinated aromatic (C-phenyl) rings are oriented towards the binuclear manganese cluster in the energetically favorable poses of the chloroquine and the most potent arginase inhibitors. Additionally, the water-mediated hydrogen bonds were formed via carbonyl function present in the new N-arylcinnamamides and the fluorine substituent (alone or in trifluoromethyl group) of N-phenyl ring seems to play a key role in forming the halogen bonds
Provoking Consciousness: Towards a Bioregional Understanding of Local Character: Urbanisation of the Fringe at Willunga Basin, South Australia
In 2010, a team of local government stakeholders set out to prepare a bid for United Nations Educational, Scientific and Cultural Organisation (UNESCO) World Heritage Site recognition and conservation of the distinctive settler-colonial agrarian landscape of the Mount Lofty Ranges, which bound the urban hinterland of Adelaide, South Australia. In the context of that ongoing bid process and global concern over loss of precious foodgrowing regions to urban development, this thesis focuses on the rural–urban development contest in the Willunga Basin, a key subregion of the proposed World Heritage Site. With particular reference to the question of ‘local character’ at the urban fringe, the study investigates the mechanisms at play in the Basin in maintaining a resilient dialogue between urban and rural development priorities. Exploring the proposition that distinctive cultural landscapes such as the Willunga Basin could be described, alternatively, as exemplary ‘bioregions’, the study applies crucial principles of bioregional planning as a theoretical framework through which local knowledge of the land together with the intangible goals of ‘living-in-place’, ‘land ethics’ and ‘place attachment’ may be engaged as analytical approaches to understand the nature and significance of ‘local character’ in the built environment. The Willunga Basin community is eager to protect and enhance this putative ‘bioregion’ and to protect the qualities that are central to the UNESCO bid—a working agrarian landscape, a distinctive cultural landscape, and a site of natural beauty with high value placed on local character and compatible urban development and architectural projects. However, this has not been an easy process. Development policies established in the 1960s highlighted the ‘local character’ of the region while seeking to protect townships within the Willunga Basin from urban sprawl. However, these policies also precipitated the urban expansion of the coastal township of nearby Aldinga, dividing the Basin into two regions and ultimately bringing the rural– urban conflict to a head at the boundaries of that division. By closely studying the elements of this conflict, this research identifies a gap between the aims and principles of such planning policies and development approval processes in practice. Taking a multidisciplinary approach—grounded in architectural and urban planning research, but drawing on the tools of ethnological and social inquiry, as well as historical and correlational research—the primary research consists of in-depth case studies of recent development proposals and the controversies raised. The six cases examined encompass a range of different development situations, types and outcomes—from housing layouts and streetscapes to retail outlets and a multi-storeyed building proposal—to explore the various policy issues and community voices raised in the public consultation process. The findings reveal multiple points of failure in practice, including lack of effective reference points of what contributes to local character; the production of sub-standard everyday architecture, resulting from a mismatch between development policy and the practice of development approvals; ineffectual and often tokenistic community consultation; and poor engagement between the local community and mostly passive developers with little contextual knowledge. The study indicates how a bioregional understanding of a cultural landscape, and the potential for sustainable development within it, underscores the particular significance of ‘local character’ in such contexts, and of a ‘conscious community’ prepared to engage in the challenge of interpreting it. By improving the process of identifying and retaining local character through meaningful dialogues between all stakeholders—local communities, developers and approving authorities—the study concludes that a sustainable balance between urban and rural/regional development is possible.Thesis (Ph.D.) -- University of Adelaide, School of Architecture and Civil Engineering, 202
Machine Learning for Kinase Drug Discovery
Cancer is one of the major public health issues, causing several million losses every year. Although anti-cancer drugs have been developed and are globally administered, mild to severe side effects are known to occur during treatment. Computer-aided drug discovery has become a cornerstone for unveiling treatments of existing as well as emerging diseases. Computational methods aim to not only speed up the drug design process, but to also reduce time-consuming, costly experiments, as well as in vivo animal testing. In this context, over the last decade especially, deep learning began to play a prominent role in the prediction of molecular activity, property and toxicity.
However, there are still major challenges when applying deep learning models in drug discovery. Those challenges include data scarcity for physicochemical tasks, the difficulty of interpreting the prediction made by deep neural networks, and the necessity of open-source and robust workflows to ensure reproducibility and reusability.
In this thesis, after reviewing the state-of-the-art in deep learning applied to virtual screening, we address the previously mentioned challenges as follows: Regarding data scarcity in the context of deep learning applied to small molecules, we developed data augmentation techniques based on the SMILES encoding. This linear string notation enumerates the atoms present in a compound by following a path along the molecule graph. Multiplicity of SMILES for a single compound can be reached by traversing the graph using different paths. We applied the developed augmentation techniques to three different deep learning models, including convolutional and recurrent neural networks, and to four property and activity data sets. The results show that augmentation improves the model accuracy independently of the deep learning model, as well as of the data set size. Moreover, we computed the uncertainty of a model by using augmentation at inference time. In this regard, we have shown that the more confident the model is in its prediction, the smaller is the error, implying that a given prediction can be trusted and is close to the target value. The software and associated documentation allows making predictions for novel compounds and have been made freely available.
Trusting predictions blindly from algorithms may have serious consequences in areas of healthcare. In this context, better understanding how a neural network classifies a compound based on its input features is highly beneficial by helping to de-risk and optimize compounds. In this research project, we decomposed the inner layers of a deep neural network to identify the toxic substructures, the toxicophores, of a compound that led to the toxicity classification. Using molecular fingerprints —vectors that indicate the presence or absence of a particular atomic environment —we were able to map a toxicity score to each of these substructures. Moreover, we developed a method to visualize in 2D the toxicophores within a compound, the so- called cytotoxicity maps, which could be of great use to medicinal chemists in identifying ways to modify molecules to eliminate toxicity. Not only does the deep learning model reach state-of-the-art results, but the identified toxicophores confirm known toxic substructures, as well as expand new potential candidates.
In order to speed up the drug discovery process, the accessibility to robust and modular workflows is extremely advantageous. In this context, the fully open-source TeachOpenCADD project was developed. Significant tasks in both cheminformatics and bioinformatics are implemented in a pedagogical fashion, allowing the material to be used for teaching as well as the starting point for novel research. In this framework, a special pipeline is dedicated to kinases, a family of proteins which are known to be involved in diseases such as cancer. The aim is to gain insights into off-targets, i.e. proteins that are unintentionally affected by a compound, and that can cause adverse effects in treatments. Four measures of kinase similarity are implemented, taking into account sequence, and structural information, as well as protein-ligand interaction, and ligand profiling data. The workflow provides clustering of a set of kinases, which can be further analyzed to understand off-target effects of inhibitors. Results show that analyzing kinases using several perspectives is crucial for the insight into off-target prediction, and gaining a global perspective of the kinome.
These novel methods can be exploited in the discovery of new drugs, and more specifically diseases involved in the dysregulation of kinases, such as cancer
Applications of artificial intelligence to alchemical free energy calculations in contemporary drug design
The work presented in this thesis resides at the interface of alchemical free energy
methods (AFE) and machine-learning (ML) in the context of computer-aided drug
discovery (CADD). The majority of the work consists of explorations into regions
of synergy between the individual parts. The overarching hypothesis behind this
work is that although areas of high potential exist for standalone ML and AFE in
CADD, an additional source of value can be found in areas where ML and AFE are
combined in such a way that the new methodology profits from key strengths in
either part.
Physics-based AFE calculations have - over several decades - grown into precise
and accurate sub-kcal·mol−1
(in terms of mean absolute error versus experimental
measures) methods of predicting ligand-protein binding affinities which is the main
driver of its popularity in project support in drug design workflows. Data-driven
ML methods have seen a similar rapid development spurred by the exponential
growth in computational hardware capabilities, but are generally still lacking in
accuracy versus experimental measures of binding affinities to support drug design
work. Contrastingly, however, the first relies mainly on physical rules in the form
of statistical mechanics and the latter profits from interpolating signals within large
training domains of data.
After a historical and theoretical introduction into drug discovery, AFE calculations
and ML methods, the thesis will highlight several studies that reflect the above hypothesis along multiple key points in the AFE workflow. Firstly, a methodology that combines AFE with ML has been developed to compute accurate absolute hydration free energies. The hybrid AFE/ML methodology
was trained on a subset of the FreeSolv database, and retrospectively shown to
outperform most submissions from the SAMPL4 competition. Compared to pure
machine-learning approaches, AFE/ML yields more precise estimates of free energies
of hydration, and requires a fraction of the training set size to outperform standalone
AFE calculations. The ML-derived correction terms are further shown to be transferable to a range of related AFE simulation protocols. The approach may be used
to inexpensively improve the accuracy of AFE calculations, and to flag molecules
which will benefit the most from bespoke force field parameterisation efforts.
Secondly, early investigations into data-driven AFE network generators has been
performed. Because AFE calculations make use of alchemical transformations between ligands in congeneric series, practitioners are required to estimate an optimal
combination of transformations for each series. AFE networks constitute the collection of edges chosen such that all ligands (nodes) are included in the network and
where each edge is a AFE calculation. As there are a vast number of possible configurations for such networks this step in AFE setup suffers from several shortcomings
such as scalability and transferability between AFE softwares.
Although AFE network generation has been automated in the past, the algorithm
depends mostly on expert-driven estimation of AFE transformation reliabilities.
This work presents a first iteration of a data-driven alternative to the state-of-the-art using a graph siamese neural network architecture. A novel dataset, RBFE Space, is presented as a representative and transferable training domain for AFE
ML research. The workflow presented in this thesis matches state-of-the-art AFE
network generation performance with several key benefits. The workflow provides
full transferability of the network generator because RBFE-Space is open-sourced
and ready to be applied to other AFE softwares. Additionally, the deep learning
model represents the first robust ML predictor of transformation reliabilities in AFE
calculations. Finally, one major shortcoming of AFE calculations is its decreased reliability for
transformations that are larger than ∼5 heavy atoms. The work reported in this
thesis describes investigations into whether running charge, Van der Waals and bond
parameter transformations individually (with variable λ allocation per step) offers an
advantage to transforming all parameters in a single step, as is the current standard
in most AFE workflows. Initial results in this work qualitatively suggest that the
bound leg benefits from a MultiStep protocol over a onestep (”SoftCore”) protocol,
whereas the free leg does not show benefit. Further work was performed by Cresset
that showed no observable benefit of the MultiStep approach over the Softcore approach. Several key findings are reported in this work that illustrate the benefits of
dissecting an FEP approach and comparing the two approaches side-by-side
Rational Design in Photopharmacology with Molecular Photoswitches
Photopharmacology is an attractive approach for achieving targeted drug action with the use of light. In photopharmacology, molecular photoswitches are introduced into the structure of biologically active small molecules to allow for the optical control of their potency. Going beyond trial and error, photopharmacology has progressively applied rational drug design methodologies to devise light-controlled bioactive ligands. In this review, we categorize photopharmacological efforts from the standpoint of medicinal chemistry strategies, focusing on diffusible photochromic ligands modified with photoswitches that operate through E-Z bond isomerization. In the vast majority of cases, photoswitchable ligands are designed as analogs of existing compounds, through a variety of approaches. By analyzing in detail a comprehensive list of instructive examples, we describe the state of the art and discuss future opportunities for rational design in photopharmacology.</p
Nanoprobes for Tumor Theranostics
This book reports cutting-edge technology in nanoprobes or nanobiomaterials used for the accurate diagnosis and therapy of tumors, involving a multidisciplinary of chemistry, materials science, oncology, biology, and medicine
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