291 research outputs found
Evolutionary ecology of obligate fungal and microsporidian invertebrate pathogens
The interactions between hosts and their parasites and pathogens are omnipresent in the natural world. These symbioses are not only key players in ecosystem functioning, but also drive genetic diversity through co-evolutionary adaptations. Within the speciose invertebrates, a plethora of interactions with obligate fungal and microsporidian pathogens exist, however the known interactions is likely only a fraction of the true diversity. Obligate invertebrate fungal and microsporidian pathogen require a host to continue their life cycle, some of which have specialised in certain host species and require host death to transmit to new hosts. Due to their requirement to kill a host to spread to a new one, obligate fungal and microsporidian pathogens regulate invertebrate host populations. Pathogen specialisation to a single or very few hosts has led to some fungi evolving the ability to manipulate their host’s behaviour to maximise transmission. The entomopathogenic fungus, Entomophthora muscae, infects houseflies (Musca domestica) over a week-long proliferation cycle, resulting in flies climbing to elevated positions, gluing their mouthparts to the substrate surface, and raising their wings to allow for a clear exit from fungal conidia through the host abdomen. These sequential behaviours are all timed to occur within a few hours of sunset. The E. muscae mechanisms used in controlling the mind of the fly remain relatively unknown, and whether other fitness costs ensue from an infection are understudied.European Commissio
2017 GREAT Day Program
SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
Exploring the gut microbiota of breast cancer patients
Host-associated microbial communities play a key role in health and disease, and more recently there has been a growing appreciation for how particular microbes and microbial ‘signatures’ are associated with different cancers. However, breast cancer remains an understudied cancer type, and there is a pressing need to define, if and how, the gut microbiota maybe be linked to disease progression and treatment outcomes.
To investigate the gut microbiota and breast cancer, two clinical cohorts were profiled (using a range of sequencing and bioinformatics approaches) and additional mechanistic in vitro and in vivo studies were also undertaken. First, a local Norfolk cohort was established – BEAM, with the aim of longitudinally profiling newly diagnosed breast cancer patients (1 control and 35 breast cancer patients, as of 30 June 2023), however study recruitment was severely impacted due to the SARS-Cov-2 pandemic. My initial analysis indicated no significant shifts in microbiome profiles in the limited number of patients profiled, however I was able to establish a large culture collection through untargeted culturing. I obtained 298 strains from 50 different species which were whole genome sequenced and phylogenetically characterised. This work also led to the discovery and detailed description of one novel genus and one novel species - Allocoprobacillus halotolerans gen. nov., sp. nov and Coprobacter tertius sp. nov.
Concurrent to BEAM, the oral and gut microbiota samples from a phase 2a clinical trial (KELLY) that had been completed were processed, sequenced, and analysed which led to the creation of the CALADRIO study. The KELLY trial had one arm where all patients received treatment, a chemotherapeutic and immunotherapeutic. Overall, treatment did not cause significant gut or oral microbiota perturbations, which is usually indicative of drug-related microbiota toxicity. Differential analysis indicated that clinical benefit was driven, in part, by gut-associated Bacteroides fragilis. Further in vitro studies indicated a product present in the cell-free supernatant of B. fragilis led to greater cellular stress in breast cancer cells, but it did not result in complete cell death.
Bifidobacterium, generally considered a beneficial gut-associated bacterium, was consistently in the top ten most abundant genera of the gut microbiota in the BEAM and CALADRIO study. Thus, to define if Bifidobacterium was mechanistically associated with breast cancer outcomes, a Bifidobacterium longum subsp. longum isolate was selected and used as a live oral supplementation in a murine breast cancer model that was also treated with chemotherapy (cyclophosphamide). Oral supplementation resulted in larger primary tumours than cyclophosphamide alone suggesting that oral supplementation interfered with treatment efficacy. Genomic screening of the isolate showed that it possessed aldehyde dehydrogenase which is known to inactivate cyclophosphamide.
These data allowed me to explore how the gut microbiota of breast cancer patients may link to treatment outcomes and indicated both positive (e.g., B. fragilis) and negative (e.g., B. longum subsp. longum) impacts. Translating it into the clinic, such findings could provide avenues for improving efficacy of anti-cancer therapeutics. To test these further in vivo studies could be conducted to determine how candidate bacterial strains could influence the immune system in the context of breast cancer and building on those findings in vitro studies would investigate the intricacies of the gut-immune axis. Overall, my thesis outputs highlight the complex interactions between the microbiota and their host, and suggest new avenues for biomarker and therapy development, particularly in breast cancer
Developmental Bootstrapping of AIs
Although some current AIs surpass human abilities in closed artificial worlds
such as board games, their abilities in the real world are limited. They make
strange mistakes and do not notice them. They cannot be instructed easily, fail
to use common sense, and lack curiosity. They do not make good collaborators.
Mainstream approaches for creating AIs are the traditional manually-constructed
symbolic AI approach and generative and deep learning AI approaches including
large language models (LLMs). These systems are not well suited for creating
robust and trustworthy AIs. Although it is outside of the mainstream, the
developmental bootstrapping approach has more potential. In developmental
bootstrapping, AIs develop competences like human children do. They start with
innate competences. They interact with the environment and learn from their
interactions. They incrementally extend their innate competences with
self-developed competences. They interact and learn from people and establish
perceptual, cognitive, and common grounding. They acquire the competences they
need through bootstrapping. However, developmental robotics has not yet
produced AIs with robust adult-level competences. Projects have typically
stopped at the Toddler Barrier corresponding to human infant development at
about two years of age, before their speech is fluent. They also do not bridge
the Reading Barrier, to skillfully and skeptically draw on the socially
developed information resources that power current LLMs. The next competences
in human cognitive development involve intrinsic motivation, imitation
learning, imagination, coordination, and communication. This position paper
lays out the logic, prospects, gaps, and challenges for extending the practice
of developmental bootstrapping to acquire further competences and create
robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure
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Interpretable Machine Learning for the Social Sciences: Applications in Political Science and Labor Economics
Recent advances in machine learning offer social scientists a unique opportunity to use data-driven methods to uncover insights into human behavior. However, current machine learning methods are opaque, ineffective on small social science datasets, and tailored for predicting unseen values rather than estimating parameters from data. In this thesis, we develop interpretable machine learning techniques designed to uncover latent patterns and estimate critical quantities in the social sciences.
We focus on two aspects of interpretability: explaining individual model predictions and discovering latent patterns from data. We describe a method for explaining the predictions of general, black-box sequence models. This method approximates a combinatorial objective to elucidate the decision-making processes of sequence models. Next, we narrow our focus to domain-specific applications. In political science, we develop the text-based ideal point model, a model that quantifies political positions from text.
This model marries a classical idea from political science with a Bayesian matrix factorization technique to infer meaningful structure from text. In labor economics, we adapt a model from natural language processing to analyze career trajectories. We describe a transfer learning method that can overcome the constraints posed by small survey datasets. Finally, we adapt this predictive model to estimate an important quantity in labor economics: the history-adjusted gender wage gap
Adversarial Random Forests for Density Estimation and Generative Modeling
We propose methods for density estimation and
data synthesis using a novel form of unsupervised
random forests. Inspired by generative adversarial
networks, we implement a recursive procedure in
which trees gradually learn structural properties
of the data through alternating rounds of generation and discrimination. The method is provably
consistent under minimal assumptions. Unlike
classic tree-based alternatives, our approach provides smooth (un)conditional densities and allows
for fully synthetic data generation. We achieve
comparable or superior performance to state-ofthe-art probabilistic circuits and deep learning
models on various tabular data benchmarks while
executing about two orders of magnitude faster
on average. An accompanying R package, arf,
is available on CRAN
Maximally Machine-Learnable Portfolios
When it comes to stock returns, any form of predictability can bolster
risk-adjusted profitability. We develop a collaborative machine learning
algorithm that optimizes portfolio weights so that the resulting synthetic
security is maximally predictable. Precisely, we introduce MACE, a multivariate
extension of Alternating Conditional Expectations that achieves the
aforementioned goal by wielding a Random Forest on one side of the equation,
and a constrained Ridge Regression on the other. There are two key improvements
with respect to Lo and MacKinlay's original maximally predictable portfolio
approach. First, it accommodates for any (nonlinear) forecasting algorithm and
predictor set. Second, it handles large portfolios. We conduct exercises at the
daily and monthly frequency and report significant increases in predictability
and profitability using very little conditioning information. Interestingly,
predictability is found in bad as well as good times, and MACE successfully
navigates the debacle of 2022
Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus
Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the
infection persists within the host throughout their life. Although Immunocompetent people rarely are
affected by HCMV infections, their related diseases pose a major health problem worldwide for those
with compromised or suppressed immune systems such as transplant recipients. Additionally,
congenital transmission of HCMV is the most common infectious cause of birth defects globally and
is associated with a substantial economic burden.
This thesis explores the application of statistical modelling and genomics to unpick three key areas of
interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was
undertaken to delineate the molecular population structure of this highly variable virus. By including
in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute
highly variable regions of the genome, novel and important insights into the co-evolution of HCMV
with its host were uncovered.
Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated
herpesviruses. This structured information along with the development of a mutation annotation
pipeline, allowed the further development of statistical models that predict the phenotype of a virus
from its sequence. The predictive power of these models was validated for HSV1 by using external
unseen mutation data provided in collaboration with the UK Health Security Agency.
Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and
pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model
allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling
HCMV lytic replication in already established infections post-haematopoietic stem cell transplant
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