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Studying σ70-finger displacement during initial transcription using single-molecule FRET
Get PDFBacterial RNA Polymerases (RNAPs) bind to transcription initiation protein factors called σ-factors to start DNA sequence-specific transcription. Within σ-factors lies a highly conserved structural module, the ‘σ-finger’, a loop that that resides very close to the ‘heart’ of transcription, the active-centre of RNAP. The σ-finger is implicated in the pre-organisation of template DNA and the synthesis of the first short RNAs. The σ-finger also blocks entry of the nascent RNA to the RNA-exit channel of the RNAP and must be displaced to allow entry into transcription elongation. Despite structural studies, σ-finger conformational changes during late transcription initiation are still unknown. To uncover the dynamic conformational landscape and mechanism of the E. coli σ-finger during initial transcription and promoter escape, this thesis uses a new single-molecule FRET (smFRET) ruler. The results show that the σ-finger is displaced from its position inside the active site cleft, before promoter escape and after synthesis of RNA lengths that are highly dependent on the sequence of the promoter DNA used. Additionally, the chemical moiety at 5’-end of RNA, which are used in different modes of transcription, was also found to influence the point of σ-finger displacement. Real-time smFRET measurements revealed the presence of significant heterogeneity in the timing of σ-finger displacement and show that different initial conformations of the σ-finger are linked to significantly different kinetics in transcription initiation and promoter escape.
This thesis identifies different mechanisms of σ-finger displacement that influence the kinetics of initial transcription and have the potential to impact gene regulation in bacteria. Since archaeal and eukaryotic transcription systems contain σ-finger-like structural modules, these mechanisms may be general and apply to all kingdoms of life
Digital transformation in the legal sector: challenges and opportunities for cybersecurity and data protection
Get PDF[Purpose] The purpose of this study was to examine the principal issues of cybersecurity and data protection in the context of digital transformation in the legal system of the Kyrgyz Republic.
[Methodology/approach/design] The study conducted legal and comparative analysis of the regulatory framework and its application in modern conditions. Additionally, the content of reports from governmental and international organisations was examined to assess current trends in data protection and digital services. The study analysed key documents such as the National Development Strategy of the Kyrgyz Republic for 2018-2040 and the Concept of Digital Transformation of the Kyrgyz Republic for 2024-2028, the Civil and Criminal Codes of the Kyrgyz Republic, as well as laws of the Kyrgyz Republic regulating access to information, data protection, and cybersecurity, namely, “On the Right of Access to Information”, “On Protection of State Secrets of the Kyrgyz Republic”, “On the National Archive Fund of the Kyrgyz Republic”, “On the Electrical Connection”. Special attention was paid to the Tunduk system as an essential element of digital public administration and interaction between public authorities.
[Findings] The key findings showed that despite great strides in digitalisation, the existing legislative framework of the Kyrgyz Republic needs considerable adaptation to ensure data protection and cybersecurity in a rapidly evolving digital environment.
[Practical implications] The study offered recommendations, including the creation of a national cybersecurity centre, the introduction of international data protection standards, and the development of human resources in the field of information security.
[Originality/value] The findings confirm the need to modernise the legal framework to ensure a sustainable and secure digital transformation in the Kyrgyz Republic, which is critical for protecting citizens’ data and increasing trust in public services
Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data
Get PDFPeople living with HIV can acquire secondary infections through a process called superinfection, giving rise to simultaneous infection with genetically distinct variants (multiple infection). Multiple infection provides the necessary conditions for the generation of novel recombinant forms of HIV and may worsen clinical outcomes and increase the rate of transmission to HIV seronegative sexual partners. To date, studies of HIV multiple infection have relied on insensitive bulk-sequencing, labor intensive single genome amplification protocols, or deep-sequencing of short genome regions. Here, we identified multiple infections in whole-genome or near whole-genome HIV RNA deep-sequence data generated from plasma samples of 2,029 people living with viremic HIV who participated in the population-based Rakai Community Cohort Study (RCCS). We estimated individual- and population-level probabilities of being multiply infected and assessed epidemiological risk factors using the novel Bayesian deep-phylogenetic multiple infection model (deep − phyloMI) which accounts for bias due to partial sequencing success and false-negative and false-positive detection rates. We estimated that between 2010 and 2020, 4.09% (95% highest posterior density interval (HPD) 2.95%–5.45%) of RCCS participants with viremic HIV multiple infection at time of sampling. Participants living in high-HIV prevalence communities along Lake Victoria were 2.33-fold (95% HPD 1.3–3.7) more likely to harbor a multiple infection compared to individuals in lower prevalence neighboring communities. This work introduces a high-throughput surveillance framework for identifying people with multiple HIV infections and quantifying population-level prevalence and risk factors of multiple infection for clinical and epidemiological investigations
Data-driven simulations and policy gradients for limit order books
Get PDFOver the last decades, central limit order books (LOBs) became a main method for asset execution in exchanges around the world for a wide variety of asset classes (e.g. [39, 85, 213]). Their practical relevance and inherent complex strategic interactions made LOBs a primary objective of study for academia and industry. The classical approach to modelling limit order books is based on identifying the most important features of the problem, encoding them in a parametric model and calibrating this model to market data. However, parametric modelling can introduce biases leading to a mismatch between actual market data and model output (e.g. [48]). Recent success in generative artificial intelligence (e.g. [75, 100]) and reinforcement learning (e.g. [93, 134, 194]) combined with the wide availability of LOB data motivates the usage of a new paradigm for decision making in LOBs, namely, fitting a generative, non-parametric model to the historical data and using reinforcement learning for optimization. In this thesis, we explore different aspects of this new paradigm.
Firstly, we propose a novel generative model, K-nearest neighbor resampling, for estimating the performance
of a policy from historical data containing realized episodes of a decision process generated under a different policy. We provide statistical consistency results under weak conditions by generalizing a well-known result in non-parametric statistics on local averaging to include episodic data and counterfactual estimation. Compared to similar methods, our algorithm does not require optimization, can be efficiently implemented via tree-based nearest neighbor search and parallelization, and does not explicitly assume a parametric model for the environment’s dynamics. Numerical experiments demonstrate the effectiveness of the algorithm compared to existing baselines in a variety of stochastic control settings.
Secondly, we show how K-nearest neighbor (K-NN) resampling can be applied to simulate LOB markets and
how it can be used to evaluate and calibrate trading strategies. Using historical LOB data, we demonstrate that our simulation method is capable of recreating realistic LOB dynamics and that synthetic trading within the simulation leads to a market impact in line with the corresponding literature. In the context of a LOB with
pro-rata type matching, we demonstrate how our algorithm can calibrate the size of limit orders for a liquidation strategy.
Thirdly, we move from generative modelling to optimization. In particular, we discuss applications of policy gradient methods for the optimal execution of an asset position via limit orders. We apply policy gradient
methods in a parametric LOB model and a realistic generative adversarial neural network (GAN) LOB model. In the first case, we apply a zeroth-order gradient estimator and modify the algorithm to lower the variance in the estimate. In the second case, we alter a policy gradient method with a path-wise gradient estimator to overcome issues with the roughness of the loss-landscape. In both cases, we are able to learn effective trading strategies.
Finally, in LOBs, interactions may occur on the level of microseconds [39] and should, thus, be seen as
practically time-continuous. We provide new insights on the usage of policy gradient algorithms in continuous
time. In particular, we give sufficient conditions to prove the linear convergence of a policy gradient method in an exploratory finite-horizon linear quadratic control (LQC) problem in continuous time. We give numerical evidence for the linear convergence of the policy gradient method. Furthermore, considering policies with piece-wise constant parameters in time, we show numerically that rescaling the gradient for the policy gradient method by the time-discretization of the policy leads to a robust linear convergence over different action frequencies
Atomically dispersed iridium electrocatalysts for the oxygen evolution reaction
No full textProton-exchange membrane (PEM) water electrolysers can efficiently integrate with renewable energy sources for green hydrogen production. With the increasing occurrence of surplus renewable electricity periods, there is a growing market for PEM water electrolysers. However, the scalability of these water electrolysers faces a bottleneck due to the use of costly Ir-based catalysts for the anodic oxygen evolution reaction (OER). To address this cost issue and to enhance catalytic efficiency, research efforts are shifting towards exploring atomically dispersed Ir electrocatalysts for the acidic OER.
While in theory, the atomically dispersed Ir electrocatalysts exhibit enhanced mass-specific activity due to the maximum utilisation of the Ir, in practice, they often show poor stability during synthesis and operation. Their degradation can be traced back to the aggregation of the atomically dispersed Ir into larger particles, i.e., a reduction of the catalytically active surface area of Ir. Enhancing catalyst stability requires a deeper understanding of the mobility, agglomeration behaviour, and bonding environment of Ir during synthesis and usage.
This thesis focuses on the synthesis and the characterisation of the atomically dispersed Ir on TiO2 anatase nanosheets for the acidic OER. In the first half of this work, a facile synthesis strategy for preparing atomically dispersed Ir on TiO2 anatase with minimised Ir agglomeration is developed. Utilising in-situ heating annular dark field scanning transmission electron microscopy (ADF-STEM) microscopy, combined with X-ray absorption fine structure (XAFS), the Ir agglomeration behaviour and mobility on TiO2 with increasing synthesis temperature is correlated with the evolution of the bonding environment of the Ir, offering critical insights into the synthesis mechanism.
In the second half of this thesis, a comparative study of OER performance between atomically dispersed Ir on TiO2 and oxidised Ir clusters is presented. By employing high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) to establish a statistically significant representation of the Ir dispersion on TiO2, correlated with the XAFS analysis of the local environment of Ir, this work provides a fundamental understanding of the structure-activity-stability of the atomically dispersed Ir on TiO2 anatase for the acidic OER
Uncivil (nonviolent) protest, communal participation, and non-participation, in Steve Biko’s ethics of just struggle
No full textThis chapter introduces a coherent ethical view of Steve Biko’s ideas about struggle and resistance by elaborating and examining the three key terms that emerge from Biko’s written and oral arguments. Alongside his written thought, I examine Biko’s testimony at the 1976 SASO/BPC apartheid court case where his Black Consciousness thought was placed on trial. I argue that what emerges is an understanding of specifically just struggle framed by two distinct ethical principles and modes of action—uncivil (nonviolent) protest and communal participation. Further, the programmatic distinction Biko maintains between nonviolent and violent protest highlights a third, important, ethical term— non-participation. I argue that these terms are not only crucial to deeper understanding of Biko’s own thought and of its ethical commitments to hope and historical progress but, also, to thinking beyond the dominant framework of much contemporary debate on civil and uncivil disobedience
Marine‐derived nutrients shape the functional composition of High Arctic plant communities
Get PDFLow temperatures and nutrient limitation have shaped Arctic plant communities, which are now affected by biome‐wise changes in both climate and nutrient cycling. Rising temperatures are favouring taller plant species with more resource‐acquisitive traits across the Arctic tundra. Simultaneously, declines in seabird populations may reduce subsidies of marine‐derived nutrients to terrestrial ecosystems, potentially favouring more resource‐conservative plant traits. It is crucial to understand the consequences of these concurrent changes in climate and marine‐derived nutrient inputs from seabirds for the functional composition and roles of Arctic plant communities. We use a 'space‐for‐time approach' to compare the functional composition of vascular plant communities across two elevational gradients in High Arctic Svalbard, one where climate is the major environmental driver and one influenced by nutrient input from a seabird colony. We assess changes in 13 traits related to plant size, leaf economics and nutrient cycling along the two gradients, and we also explore the relative contributions of species turnover and intraspecific variation to total trait variation across and between the gradients. Elevation per se had little impact on the plant functional composition. Instead, plants at the top of the seabird nutrient gradient, closest to the nesting sites, were taller and had resource‐acquisitive trait values, such as larger and thicker leaves and higher leaf nutrient contents. Enriched soil δ15N‰ signatures at these sites correlated with resource‐acquisitive values of leaf area, specific leaf area, leaf dry matter content, leaf phosphorous content and with enriched leaf δ15N‰ signatures. This variation in leaf economic traits and isotopes was largely driven by intraspecific variation at the nutrient gradient, whereas species turnover dominated at the reference gradient. Our results are consistent with marine‐derived nutrient subsidies from seabirds being a major driver of functional trait variation in Arctic vegetation. Ongoing declines in seabird populations may therefore affect terrestrial primary producer communities in the Arctic and beyond, with potentially important but unknown implications for biodiversity, consumer and decomposer communities, and ecosystem processes. Read the free Plain Language Summary for this article on the Journal blog
Short-Term Forecasting Arabica Coffee Cherry Yields by Seq2Seq over LSTM for Smallholder Farmers
Get PDFCoffee production is a vital source of income for smallholder farmers in Mexico’s Chiapas, Oaxaca, Puebla, and Veracruz regions. However, climate change, fluctuating yields, and the lack of decision-support tools pose challenges to the implementation of sustainable agricultural practices. The SABERES project aims to address these challenges through a Seq2Seq-LSTM model for predicting coffee yields in the short term, using datasets from Mexican national institutions, including the Agricultural Census (SIAP) and environmental data from the National Water Commission (CONAGUA). The model has demonstrated high accuracy in replicating historical yields for Chiapas and can forecast yields for the next two years. As a first step, we assessed coffee yield prediction for Bali, Indonesia, by comparing the LSTM, ARIMA, and Seq2Seq-LSTM models using historical data. The results show that the Seq2Seq-LSTM model provided the most accurate predictions, outperforming LSTM and ARIMA. Optimal performance was achieved using the maximum data sequence. Building on these findings, we aimed to apply the best configuration to forecast coffee yields in Chiapas, Mexico. The Seq2Seq-LSTM model achieved an average difference of only 0.000247, indicating near-perfect accuracy. It, therefore, demonstrated high accuracy in replicating historical yields for Chiapas, providing confidence for the next two years’ predictions. These results highlight the potential of Seq2Seq-LSTM to improve yield forecasts, support decision making, and enhance resilience in coffee production under climate change
HCM-Associated MuRF1 Variants Compromise Ubiquitylation and Are Predicted to Alter Protein Structure
Get PDFMuRF1 [muscle RING (Really Interesting New Gene)-finger protein-1] is an ubiquitin-protein ligase (E3), which encode by TRIM63 (tripartite motif containing 63) gene, playing a crucial role in regulating cardiac muscle size and function through ubiquitylation. Among hypertrophic cardiomyopathy (HCM) patients, 24 TRIM63 variants have been identified, with 1 additional variant linked to restrictive cardiomyopathy. However, only three variants have been previously investigated for their functional effects. The structural impacts of the 25 variants remain unexplored. This study investigated the effects of 25 MuRF1 variants on ubiquitylation activity using in vitro ubiquitylation assays and structural predictions using computational approaches. The variants were generated using site-directed PCR (Polymerase Chain Reaction) mutagenesis and subsequently purified with amylose affinity chromatography. In vitro ubiquitylation assays demonstrated that all 25 variants compromised the ability of MuRF1 to monoubiquitylate a titin fragment (A168-A170), while 17 variants significantly impaired or completely abolished auto-monoubiquitylation. Structural modelling predicted that 10 MuRF1 variants disrupted zinc binding or key stabilising interactions, compromising structural integrity. In contrast, three variants were predicted to enhance the structural stability of MuRF1, while six others were predicted to have no discernible impact on the structure. This study underscores the importance of functional assays and structural predictions in evaluating MuRF1 variant pathogenicity and provides novel insights into mechanisms by which these variants contribute to HCM and related cardiomyopathies
Identifying context-specific determinants to inform improvement of antimicrobial stewardship implementation in healthcare facilities in Asia: results from a scoping review and web-based survey among local experts
Get PDFInternational guidelines are available for the assessment and improvement of antimicrobial stewardship (AMS) programmes: an important strategy to address the escalating global antimicrobial resistance problem. However, existing AMS assessment tools lack contextual specificity for resource-limited settings, leading to limited applicability in Asia. This project aimed to identify relevant themes from current guidance documents to help develop a context-specific assessment tool that can be applied by healthcare facilities (HCFs) to improve local implementation. We performed a sequential approach of a scoping review to identify relevant assessment themes for Asia and an expert survey for getting feedback on the relevance of assessment stems developed from the scoping review. We reviewed English-language published documents discussing AMS implementation or assessment at HCFs globally and in Asia. Themes were derived through content analysis and classified following the predefined context dimensions to develop assessment stems, defined as containing one identified determinant that may influence implementation outcomes. The survey consisting of identified assessment stems was reviewed by 20 locally identified experts in Asia who rated the level of relevance of these stems in AMS implementation in the region. National leadership, training and technical support, and policy and guidance were the most commonly identified themes among 100 themes identified from 73 reviewed documents. From these themes, we developed 131 assessment stems for the expert survey. Of the 131 assessment stems, 117 (89%) were considered relevant for AMS implementation in Asia by at least 80% of respondents. These stems were included in the process of developing a global AMS assessment tool to support HCFs to improve their programmes. In conclusion, national leadership and support represent a distinct and important aspect affecting AMS implementation in HCFs in Asia. The identified assessment themes have substantial value for the formulation of locally relevant implementation strategies tailored to the Asian context