HARVEST
University of Saskatchewan's Repository for Research, Scholarship, and Artistic Work
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“New People”: Memory, Leisure, and Identity at the British Seaside in Last Orders and the Remains of the Day
(2025-06-23) McLean, Ava H. D.; Martin, Ann R.C.; Ophir, Ella Z.; Banco, Lindsey M.; Rajiva, Jay
In both The Remains of the Day (1989) and Last Orders (1996), Kazuo Ishiguro and Graham Swift tie explorations of memory, interpersonal relationships, and individual identity to journeys to the sea. To articulate how their characters view the seaside not only as a mode of “recuperation and liberation from the stresses of daily life” (Baranowski and Furlough 5), but also as an opportunity for self-improvement and even reinvention, Ishiguro and Swift each rely on tropes in which destinations such as Weymouth and Margate are synonymous with decay and disappointment. I argue that the authors draw upon a literary tradition where seaside vacation destinations are associated with disillusionment, as both as both writers employ narrators whose identities are yoked to their careers and whose journeys expose the highly subjective stories that they construct about themselves in relation to work, home, and leisure. Using historical analyses of British seaside culture to illuminate the traditions that Ishiguro and Swift each reference, I focus on the authors’ uses of internal monologue to suggest that, in their hands, illusions about leisure, personal relationships, and professional identity are exposed and interrogated through journeys to the sea. Edward Engelberg’s conceptualization of the “unlived life” (i) allows for analysis of the overlapping functions of memory, storytelling, and identity in both novels. While Ishiguro’s butler, Mr. Stevens, sidesteps the implications of his life so fastidiously that he has no identity outside of his work, Swift’s many narrators seem continually trapped by narratives about the people they could have been. In each case, the promise of growth and contentment through holiday travel to the sea remains illusory, as both Stevens and Swift’s chorus of narrators remain locked in a cycle of remembering.
Coupling field and laboratory studies of immunity and infection in zoonotic hosts
(The Lancet, 2023) Becker, Daniel J.; Banerjee, Arinjay
The COVID-19 pandemic and discovery of SARS-CoV-2-like viruses in Rhinolophus bats has reinforced the need to identify wildlife sources of zoonotic pathogens and to forecast where and when spillover to humans is likely to occur. Although we have long recognised that most emerging infectious diseases, especially viral infections, originate in wildlife, the global virome remains poorly characterised. Growing quantities of host–virus association data and advancements in statistical modelling are now facilitating our ability to predict probable wildlife hosts and prioritise field sampling to uncover novel virus diversity. How best to follow such species-level predictions for downstream insights most relevant to the risk of zoonotic spillover is an outstanding challenge.
Stochastic L-system inference from multiple string sequence inputs
(Springer, 2022-12-08) Bernard, Jason; McQuillan, Ian
Lindenmayer systems (L-systems) are a grammar system that consists of string rewriting rules. The rules replace every symbol in a string in parallel with a successor to produce the next string, and this procedure iterates. In a stochastic context-free L-system (S0L-system), every symbol may have one or more rewriting rule, each with an associated probability of selection. Properly constructed rewriting rules have been found to be useful for modeling and simulating some natural and human engineered processes where each derived string describes a step in the simulation. Typically, processes are modeled by experts who meticulously construct the rules based on measurements or domain knowledge of the process. This paper presents an automated approach to finding stochastic L-systems, given a set of string sequences as input. The implemented tool is called the Plant Model Inference Tool for S0L-systems or PMIT-S0L. PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test suite, which are each used to generate input strings, and PMIT-S0L is then used to infer the system from only the sequences. The evaluation shows that PMIT-S0L infers S0L-systems with up to 9 rewriting rules each in under 12 hours. Additionally, it is found that 3 sequences of strings are sufficient to find the correct original rewriting rules in 100% of the cases in the test suite, and 6 sequences of strings reduce the difference in the associated probabilities to approximately 1% or less.
On decidability of problems involving insertion operations
(Springer, 2025-06-14) Ibarra, Oscar H.; McQuillan, Ian
In the past, there have been many different string insertion operations—often inspired by biological or physical systems—that have been defined and studied using formal language theory. For each such insertion operation, it is common to state various decision problems using important computational models, and to study whether these problems are decidable or not. Here, we generalize this approach by studying any possible insertion operation that can be defined as long as they produce every string in some lower bound language and no strings outside some upper bound language. We create two different such classes of insertion operations called general contextual insertion operations and general concatenative insertion operations. Several problems (so-called language equations) are shown to be undecidable no matter how an insertion operation is defined, so long as it belongs to one of these classes of operations. Several problems are also shown to be decidable, as long as the insertion operation satisfies some simple conditions. This general approach covers many different insertion operations that have been defined and studied in the literature, from classical operations such as concatenation and shuffle, to bio-inspired insertion operations, and we are able to demonstrate many stronger decidability results than what was previously known.
Inductive inference of lindenmayer systems: algorithms and computational complexity
(Springer, 2025-06-10) Duffy, Christopher; Hillis, Sam; Khan, Umer; McQuillan, Ian; Shan, Sonja Linghui
Lindenmayer systems (L-systems) are string rewriting systems that can model and be used to create simulations of processes with inherent parallelism and self-similarity. Inference of L-systems involves the automated learning of these models/grammars from data; and inductive inference involves learning an L-system from a sequence of strings initially generated by an unknown L-system. This paper studies the computational complexity of inductive inference of a variety of different types of context-free L-systems (deterministic or nondeterministic, tabled or not, and allowing erasing or not). Because this inference is sometimes trivial for nondeterministic L-systems, it is more useful to find the smallest L-system that can generate the sequence of strings, in terms of either the number of rewriting rules, or (when there are tables), the number of tables. For all of the types of L-systems studied, finding an L-system with the smallest number of rewriting rules is NP--complete. However, in all cases, if the number of rewriting rules is fixed, then finding an L-system of any type studied, or finding the smallest L-systems in terms of the number of rewriting rules, or the smallest in terms of the number of tables, can always be done in polynomial time.
Visit-Bounded Stack Automata
(Springer, 2023-07-23) Jirasek, Jozef; McQuillan, Ian
An automaton is k-visit-bounded if during any computation its work tape head visits each tape cell at most k times. In this paper we consider stack automata which are k-visit-bounded for some integer k. This restriction resets the visits when popping (unlike similarly defined Turing machine restrictions) which we show allows the model to accept a proper superset of context-free languages and also a proper superset of languages of visit-bounded Turing machines. We study two variants of visit-bounded stack automata: one where only instructions that move the stack head downwards increase the number of visits of the destination cell, and another where any transition increases the number of visits. We prove that the two types of automata recognize the same languages. We then show that all languages recognized by visit-bounded stack automata are effectively semilinear, and hence are letter-equivalent to regular languages, which can be used to show other properties.
New characterizations of exponential, elementary, and non-elementary time-bounded Turing machines
(Elsevier, 2023-06) Ibarra, Oscar H.; McQuillan, Ian
Machine models with multiple types of stores are studied. Deterministic two-way pushdown automata augmented by some number of checking stacks are known to accept exactly the class of elementary languages, which is very general but still has a decidable membership problem. First, we define such a machine to be synchronous if, when a checking stack starts to read from its stack, all other checking stacks can no longer write. For such a synchronous machine, the multiple checking stacks are equivalent to machines with 2 synchronous checking stacks which, in turn, are equivalent to exponential time-bounded deterministic Turing machines. Next, we also show that for any (reasonably defined) one-way deterministic machine model M with a decidable membership problem, the two-way deterministic multi-head variant of M augmented by any number of checking stacks also has a decidable membership problem. We also examine a model, two-way deterministic pushdown automata augmented with some number of non-erasing stacks, where the machine starts reading from the stacks at most a linear number of times. We show that this model accepts non-elementary languages but still has a decidable membership problem, resolving an open problem from the literature.
On the complexity of decision problems for some classes of machines and applications
(Elsevier, 2023-10) Ibarra, Oscar H.; McQuillan, Ian
We study the computational complexity of important decision problems — including general membership, fixed-machine membership, emptiness, disjointness, equivalence, containment, universe, and finiteness problems — for various restrictions and combinations of two-way nondeterministic reversal-bounded multicounter machines (2 NCM) and two-way pushdown automata. We show that the general membership problem (respectively fixed membership problem) for 2 NCM is NP-complete (respectively in P).
We then give applications to some problems in coding theory. We examine generalizations of various types of codes with marginal errors. For example, a language L is k-infix-free if there is no non-empty string y in L that is an infix of more than k strings in L - {y}. Our general results imply the complexity of determining whether a given machine accepts a k-infix-free language, for one- and two-way deterministic and nondeterministic finite automata (answering an open question from the literature).
Genomic and transcriptomic characterization of delta SARS-CoV-2 infection in free-ranging white-tailed deer (Odocoileus virginianus)
(ScienceDirect, 2023-11) Kotwa, Jonathon D.; Lobb, Briallen; Massé, Ariane; Gagnier, Marianne; Aftanas, Patryk; Banerjee, Arinjay; Banete, Andra; Blais-Savoie, Juliette; Bowman, Jeff; Buchanan, Tore; Chee, Hsien-Yao; Kruczkiewicz, Peter; Nirmalarajah, Kuganya; Soos, Catherine; Vernygora, Oksana; Yip, Lily; Lindsay, L. Robbin; McGeer, Allison J.; Maguire, Finlay; Lung, Oliver; Doxey, Andrew C.; Pickering, Bradley; Mubareka, Samira
White-tailed deer (WTD) are susceptible to SARS-CoV-2 and represent an important species for surveillance. Samples from WTD (n = 258) collected in November 2021 from Québec, Canada were analyzed for SARS-CoV-2 RNA. We employed viral genomics and host transcriptomics to further characterize infection and investigate host response. We detected Delta SARS-CoV-2 (B.1.617.2) in WTD from the Estrie region; sequences clustered with human sequences from October 2021 from Vermont, USA, which borders this region. Mutations in the S-gene and a deletion in ORF8 were detected. Host expression patterns in SARS-CoV-2 infected WTD were associated with the innate immune response, including signaling pathways related to anti-viral, pro- and anti-inflammatory signaling, and host damage. We found limited correlation between genes associated with innate immune response from human and WTD nasal samples, suggesting differences in responses to SARS-CoV-2 infection. Our findings provide preliminary insights into host response to SARS-CoV-2 infection in naturally infected WTD.
ASK1 inhibitors are potential pan-antiviral drugs, which dampen replication of diverse viruses including SARS-CoV2 Author links open overlay panel
(Elsevier, 2023) Demian, Wael L.; Jacob, Rajesh Abraham; Cormier, Olga; Nazli, Aisha; Melki, Matthew; Asavajaru, Akarin; Baid, Kaushal; Zhang, Ali; Miller, Matthew S.; Kaushic, Charu; Banerjee, Arinjay; Mossman, Karen
Apoptosis signal-regulating kinase 1 (ASK1)/MAP3K5 is a stress response kinase that is activated by various stimuli. It is known as an upstream activator of p38- Mitogen-activated protein kinase (p38MAPK) and c-Jun N-terminal kinase (JNK) that are reactive oxygen species (ROS)-induced kinases. Accumulating evidence show that ROS accumulate in virus-infected cells. Here, we investigated the relationship between viruses and ASK1/p38MAPK or ASK1/JNK pathways. Our findings suggest that virus infection activates ASK1 related pathways. In parallel, ASK1 inhibition led to a remarkable reduction in the replication of a broad range of viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), vaccinia virus (VV), vesicular stomatitis virus (VSV), Herpes Simplex Virus (HSV), and Human Immunodeficiency virus (HIV) in different human cell lines. Our work demonstrates the potential therapeutic use of Selonsertib, an ASK1 inhibitor, as a pan-antiviral drug in humans. Surprisingly, we observed differential effects of Selonsertib in in vitro and in vivo hamster models, suggesting caution in using rodent models to predict clinical and therapeutic outcomes in humans.