Characterisation of the ESAG6 and ESAG7 3'UTRs involved in the iron starvation response in Trypanosoma brucei

All organisms require iron for survival and the bloodstream form of the parasite Trypanosoma brucei has evolved a unique receptor that binds host transferrin to facilitate iron uptake. The TbTfR is encoded by two expression site associated genes, ESAG6 and ESAG7, and is able to bind transferrin with a variable affinity from a wide range of host organisms. Upon iron starvation, the parasite is able to rapidly upregulate the expression of the transferrin receptor via a post-transcriptional mechanism mediated by the ESAG6 3’UTR. Here the ESAG7 3’UTR is defined, and truncations were made of the ESAG6 and ESAG7 3’UTRs to identify motifs that are important for the upregulation of the receptor. The truncated sequences were ligated into a firefly luciferase reporter system and transfected into a tagged rRNA locus in a bloodstream form cell line. Luciferase assays were performed on the truncated cell lines to measure the upregulation of the reporter gene when iron starvation is induced. Normally, expression of the transferrin receptor is low under basal conditions and increases when the cells are incubated in media starved of iron. Under iron starvation conditions, it was observed that a number of the truncated cell lines were able to increase the expression of the reporter gene by a magnitude previously reported for the upregulation of the transferrin receptor. This response was only maintained when the 3’ end of the 3’UTR remained undisrupted. From the 3’UTR a putative motif has been identified that may be responsible for mediating the upregulation of the transferrin receptor under iron starvation conditions

Deep Learning based Automated Forest Health Diagnosis from Aerial Images

Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54\%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire

Interference and Volatility in Evolutionary Agent-Based Systems

Agents that exist and pursue individual goals in shared environments can indirectly affect one another in unanticipated ways, such that the actions of others in the environment can interfere with the ability to achieve goals. Despite this, the impact that these unintended interactions and interference can have on agents is not currently well understood. This is problematic as these goal-oriented agents are increasingly situated in complex sociotechnical systems, that are composed of many actors that are heterogeneous in nature. The primary aim of this thesis is to explore the effect that indirect interference from others has on evolution and goal-achieving behaviour in agent-based systems. More specifically, this is investigated in the context of agents that do not possess the ability to perceive or learn about others within the environment, as information about others may not be readily available at runtime, or there may be a distinct lack of capacity to obtain such information. By conducting three experimental studies, it is established that evolutionary volatility is a consequence of indirect interactions between goal-oriented agents in a shared environment, and that these consequences can be mitigated by designing more socially-sensitive agents. Specifically, agents that employ social action are demonstrated to reduce the evolutionary volatility experienced by goal-oriented agents, without aecting the tness received. Additionally, behavioural plasticity achieved via neuromodulation is shown to allow coexisting agents to achieve their goals more often with less evolutionary volatility in highly variable environments. While sufficient approaches to mitigate interference include learning about or modelling others, or for agents to be explicitly designed to identify interference to mitigate its consequences, this thesis demonstrates that these are not necessary. Instead, more socially-sensitive agents are shown to be capable of achieving their goals and mitigating interference without this knowledge of others, simply by shifting the focus from goal-oriented actions to more socially-oriented behaviour

Covid-19 lockdown UK adult experiences - Preliminary results

Thanks for taking a look at this summary of the preliminary results from our UK COVID-19 Lockdown Adult Experiences survey conducted between March 2022 and June 2022. Here we summarize the responses given by the 1,330 adults in the UK who participated in our nationwide survey. We have released a parallel report on the results from our UK COVID-19 Lockdown Experiences survey for LGBTQ+ respondents on our website lgbtq1835c19lockdown.wordpress.com This is one of a series of surveys and interview studies that we have undertaken in our international research project on adults’ experiences of the coronavirus pandemic and associated restrictions. We hope you will find the results interesting and useful. If you have any questions, would like to know more about the results, or would like to quote any of the material here, please do get in touch with the main Research Team at Birkbeck, University of London – Fiona Tasker ([email protected])

Healthcare Access, Satisfaction, and Health‑Related Quality of Life Among Children and Adults with Rare Diseases

Background: Research in a variety of countries indicates that healthcare access and health-related quality of life are challenged among people with a variety of rare diseases (RDs). However, there has been little systematic research on the experiences of children and adults with RDs in the American healthcare system that identifies commonalities across RDs. This research aimed to: (1) Describe demographics, disease characteristics, diagnostic experiences, access to healthcare, knowledge about RDs, support from healthcare professionals, and patient satisfaction among people with RDs and their caregivers; (2) examine predictors of patient satisfaction among adults with RDs; (3) compare health-related quality of life and stigma to US population norms; 4) examine predictors of anxiety and depression among adults and children with RDs. Results: This large-scale survey included (n = 1128) adults with RD or parents or caregivers of children with RDs representing 344 different RDs. About one third of participants waited four or more years for a diagnosis and misdiagnosis was common. A subset of participants reported experiencing insurance-related delays or denials for tests, treatments, specialists, or services. Approximately half of participants felt their medical and social support was sufficient, yet less than a third had sufficient dental and psychological support. Patients were generally neither satisfied or dissatisfied with their healthcare providers. Major predictors of satisfaction were lower stigma, lower anxiety, shorter diagnostic odyssey, greater physical function, and less pain interference. Adults and children with RDs had significantly poorer health-related quality of life and stigma in all domains compared to US norms. Predictors of both anxiety and depression were greater stigma/poor peer relationships, fatigue, sleep disturbance, limited ability to participate in social roles, and unstable disease course. Conclusions: People in the U.S. with RDs have poor health-related quality of life and high stigma. These factors arerelated to patient satisfaction and healthcare access, including diagnostic delays and misdiagnosis. Advocacy work is needed in order to improve healthcare access and ultimately health-related quality of life for children and adults with RDs

Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach

In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding

Beyond goal-rationality:Traditional action can reduce volatility in socially situated agents

Systems that pursue their own goals in shared environments can indirectly affect one another in unanticipated ways, such that the actions of other systems can interfere with goal-achievement. As humans have evolved to achieve goals despite interference from others in society, we thus endow socially situated agents with the capacity for social action as a means of mitigating interference in co-existing systems. We demonstrate that behavioural and evolutionary volatility caused by indirect interactions of goal-rational agents can be reduced by designing agents in a more socially-sensitive manner. We therefore challenge the assumption that designers of intelligent systems typically make, that goal-rationality is sufficient for achieving goals in shared environments

Explaining Evolutionary Agent-Based Models via Principled Simplification

Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid

Comparing the 2021 Census to administrative data to better understand the population estimation challenge

Objectives To analyse the linked dataset between England and Wales 2021 Census/Census Coverage Survey and the Demographic Index (DI). This is a rare and unique opportunity to make direct comparisons between Census and administrative data to provide evidence on improvements in the use of administrative data in population and migration statistics. Methods The DI is a composite dataset made up of several linked administrative sources. The linked output is complex, made up of individual sources grouped into clusters. These were linked to the Census, using both automatic and clerical matching to obtain the highest matching quality possible. To carry out the analysis, a flagging strategy has been designed to enable analysts to form cuts of the linked dataset that are specific to their research needs. High level research questions were designed to compare the coverage and capture across the Census and administrative data. Results Initial analysis has been completed and reviewed, including: comparing the coverage of the DI and Statistical Population Datasets (which use inclusion rules to approximate the usual resident population) to Census, comparing how Communal establishments are captured in the DI and Census and comparing the geography of those found in both the DI and the Census. The results will inform the National Statistician’s 2023 Recommendation, but also the future delivery of transformed population and migration statistics. This includes: • Providing insights to improve the DI’s linkage methodology • The SPD’s construct • Identifying the extent of over and under coverage in the SPD, allowing development of an estimation strategy to estimate the population using administrative data more accurately Conclusion This opportunity to compare linked administrative and Census data enables analysis to provide unique insights on quality and coverage, to improve the use of administrative data to inform population estimates, provide evidence on the use of additional administrative sources and improve future linkages