The majority of Canadians likely behaved as myopic rationalists rather than success-based learners when deciding on their first dose of COVID-19 vaccine.

Introduction: Successful vaccine promotion communication strategies require knowing how eligible recipients will respond to the opportunity to get vaccinated. Two main classes of recipients are myopic rationalists, those who receive a dose of vaccine only if it maximizes their own instant benefit and if so, do it as soon as possible, and success-based learners, those who learn from others that they perceive to be most successful. Methods: A recent study models these two decision-making types, and estimates the population proportion of myopic rationalists in each U.S. state. In this report, we fit a similar model to data on COVID-19 vaccine uptake across the Canadian provinces and territories. Results: We estimated that 64% of Canadians behaved as myopic rationalists in taking the first dose of a COVID-19 vaccine, compared to an estimated 47% in the United States. Among the provinces, the lowest proportion of myopic rationalists was 0.51 in Saskatchewan, while the highest was 0.74 in Prince Edward Island. The correlation analysis suggested a positive correlation between the proportion of myopic rationalists and the average age across the Canadian provinces (Pearson-r = 0.71). Discussion: Canadian health management may benefit from these results in tailoring the vaccine promotion communication strategies.

An early warning indicator trained on stochastic disease-spreading models with different noises.

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Revealing Decision-Making Strategies of Americans in Taking COVID-19 Vaccination.

Efficient coverage for newly developed vaccines requires knowing which groups of individuals will accept the vaccine immediately and which will take longer to accept or never accept. Of those who may eventually accept the vaccine, there are two main types: success-based learners, basing their decisions on others' satisfaction, and myopic rationalists, attending to their own immediate perceived benefit. We used COVID-19 vaccination data to fit a mechanistic model capturing the distinct effects of the two types on the vaccination progress. We proved the identifiability of the population proportions of each type and estimated that 47 % of Americans behaved as myopic rationalists with a high variation across the jurisdictions, from 31 % in Mississippi to 76 % in Vermont. The proportion was correlated with the vaccination coverage, proportion of votes in favor of Democrats in 2020 presidential election, and education score.

BIPOC experiences of (anti-)racist patient engagement in adolescent and young adult oncology research: an electronic Delphi study.

Aims: To characterize Black, Indigenous and People of Color (BIPOC) adolescent and young adult (AYA) cancer patients' experiences of patient engagement in AYA oncology and derive best practices that are co-developed by BIPOC AYAs and oncology professionals. Materials & methods: Following a previous call to action from AYA oncology professionals, a panel of experts composed exclusively of BIPOC AYA cancer patients (n = 32) participated in an electronic Delphi study. Results: Emergent themes described BIPOC AYA cancer patients' direct experiences and consensus opinion on recommendations to advance antiracist patient engagement from BIPOC AYA cancer patients and oncology professionals. Conclusion: The findings reveal high-priority practices across all phases of research and are instructional for advancing health equity.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

BACKGROUND: Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements. METHODS: We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic. RESULTS: Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence. CONCLUSIONS: Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Health Insurance Literacy Improvements Among Recently Diagnosed Adolescents and Young Adults With Cancer: Results From a Pilot Randomized Controlled Trial.

PURPOSE: Adolescents and young adults (AYAs; age 18-39 years) with cancer report needing support with health insurance. We conducted a pilot randomized controlled trial to assess the feasibility and acceptability of a virtual health insurance navigation intervention (HIAYA CHAT) to improve health insurance literacy (HIL), awareness of Affordable Care Act (ACA) protections, financial toxicity, and stress. MATERIALS AND METHODS: HIAYA CHAT is a four-session navigator delivered program; it includes psychoeducation on insurance, navigating one's plan, insurance-related laws, and managing costs. Participants were eligible if they could access an internet-capable device, were <1 year from diagnosis, and received treatment from University of Utah Healthcare or Intermountain Health systems. We assessed the feasibility, acceptability, and preliminary efficacy of HIAYA CHAT compared with usual navigation care, including HIL (nine items), insurance knowledge (13 items), ACA protections (eight items), COmprehensive Score for financial Toxicity (COST; 11 items), and Perceived Stress Scale (PSS; four items), using t tests and Cohen's d. RESULTS: From November 2020 to December 2021, N = 86 AYAs enrolled (44.6% participation) and 89.3% completed the 5-month follow-up survey; 68.6% were female, 72.1% were White, 23.3% were Hispanic, 65.1% were age 26-39 years, and 87.2% were privately insured. Of intervention participants (n = 45), 67.4% completed all four sessions; among an exit interview subset (n = 10), all endorsed the program (100%). At follow-up, compared with usual navigation care, intervention participants had greater improvements in HIL, insurance and ACA protections knowledge, and PSS; effect sizes ranged from moderate to large (0.42-0.77). COST did not differ. CONCLUSION: The results support the feasibility and acceptability of HIAYA CHAT with related improvements in HIL.

Clinical characteristics, racial inequities, and outcomes in patients with breast cancer and COVID-19: A COVID-19 and cancer consortium (CCC19) cohort study.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. Funding: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. Clinical trial number: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701.

Biomarker Testing Trends in Patients With Metastatic Colorectal Cancer Who Live in Rural Areas and Urban Clusters in the US.

BACKGROUND: There is a paucity of data on biomarker testing rates in rural populations with metastatic colorectal cancer (mCRC). To assess biomarker testing practices, oncologists in rural areas and urban clusters in the US were surveyed. MATERIALS AND METHODS: A web-based survey was administered to oncologists spending ≥40% of their time practicing in rural areas or urban clusters and who had treated ≥2 patients with stage IV mCRC in the prior month. RESULTS: Ninety-nine oncologists completed the quantitative survey and 17 the qualitative interview. Among respondents, 97% reported ordering biomarker tests. Oncologists reported testing for KRAS, NRAS, BRAF, HER2, and mismatch repair deficiency/microsatellite instability in 72%, 65%, 63%, 56%, and 66% of patients with metastatic disease, respectively. Forty-one percent reported performing reflex testing. The most cited testing barriers were lack of insurance coverage, insufficient tissue samples, and long turnaround times. CONCLUSION: Further assessment of rural testing practices is needed.

Coupling Mountain Pine Beetle and Forest Population Dynamics Predicts Transient Outbreaks that are Likely to Increase in Number with Climate Change.

Mountain pine beetle (MPB) in Canada have spread well beyond their historical range. Accurate modelling of the long-term dynamics of MPB is critical for assessing the risk of further expansion and informing management strategies, particularly in the context of climate change and variable forest resilience. Most previous models have focused on capturing a single outbreak without tree replacement. While these models are useful for understanding MPB biology and outbreak dynamics, they cannot accurately model long-term forest dynamics. Past models that incorporate forest growth tend to simplify beetle dynamics. We present a new model that couples forest growth to MPB population dynamics and accurately captures key aspects of MPB biology, including a threshold for the number of beetles needed to overcome tree defenses and beetle aggregation that facilitates mass attacks. These mechanisms lead to a demographic Allee effect, which is known to be important in beetle population dynamics. We show that as forest resilience decreases, a fold bifurcation emerges and there is a stable fixed point with a non-zero MPB population. We derive conditions for the existence of this equilibrium. We then simulate biologically relevant scenarios and show that the beetle population approaches this equilibrium with transient boom and bust cycles with period related to the time of forest recovery. As forest resilience decreases, the Allee threshold also decreases. Thus, if host resilience decreases under climate change, for example under increased stress from drought, then the lower Allee threshold makes transient outbreaks more likely to occur in the future.

Fibrosis induced by resident macrophages has divergent roles in pancreas inflammatory injury and PDAC.

Tissue-resident macrophages (TRMs) are long-lived cells that maintain locally and can be phenotypically distinct from monocyte-derived macrophages. Whether TRMs and monocyte-derived macrophages have district roles under differing pathologies is not understood. Here, we showed that a substantial portion of the macrophages that accumulated during pancreatitis and pancreatic cancer in mice had expanded from TRMs. Pancreas TRMs had an extracellular matrix remodeling phenotype that was important for maintaining tissue homeostasis during inflammation. Loss of TRMs led to exacerbation of severe pancreatitis and death, due to impaired acinar cell survival and recovery. During pancreatitis, TRMs elicited protective effects by triggering the accumulation and activation of fibroblasts, which was necessary for initiating fibrosis as a wound healing response. The same TRM-driven fibrosis, however, drove pancreas cancer pathogenesis and progression. Together, these findings indicate that TRMs play divergent roles in the pathogenesis of pancreatitis and cancer through regulation of stromagenesis.

Shared Decision Making in the Care of Patients With Cancer.

Shared decision making (SDM) is a method of care that is suitable for the care of patients with cancer. It involves a collaborative conversation seeking to respond sensibly to the problematic situation of the patient, cocreating a plan of care that makes sense intellectually, practically, and emotionally. Genetic testing to identify whether a patient has a hereditary cancer syndrome represents a prime example of the importance for SDM in oncology. SDM is important for genetic testing because not only results affect current cancer treatment, cancer surveillance, and care of relatives but also these tests generate both complex results and psychological concerns. SDM conversations should take place without interruptions, disruptions, or hurry and be supported, where available, by tools that assist in conveying the relevant evidence and in supporting plan development. Examples of these tools include treatment SDM encounter aids and the Genetics Adviser. Patients are expected to play a key role in making decisions and implementing plans of care, but several evolving challenges related to the unfettered access to information and expertise of varying trustworthiness and complexity in between interactions with clinicians can both support and complicate this role. SDM should result in a plan of care that is maximally responsive to the biology and biography of each patient, maximally supportive of each patient's goals and priorities, and minimally disruptive of their lives and loves.

Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1,383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32 - 1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70 - 6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83 - 12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63 - 3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20 - 2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66 - 3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89 - 22.6]). Hispanic ethnicity, timing and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to Non-Hispanic White patients.

Consensus report of the 2021 National Cancer Institute neuroendocrine tumor clinical trials planning meeting.

Important progress has been made over the last decade in the classification, imaging, and treatment of neuroendocrine neoplasm (NENs), with several new agents approved for use. Although the treatment options available for patients with well-differentiated neuroendocrine tumors (NETs) have greatly expanded, the rapidly changing landscape has presented several unanswered questions about how best to optimize, sequence, and individualize therapy. Perhaps the most important development over the last decade has been the approval of 177Lu-DOTATATE for treatment of gastroenteropancreatic-NETs, raising questions around optimal sequencing of peptide receptor radionuclide therapy (PRRT) relative to other therapeutic options, the role of re-treatment with PRRT, and whether PRRT can be further optimized through use of dosimetry among other approaches. The NET Task Force of the National Cancer Institute GI Steering Committee convened a clinical trial planning meeting in 2021 with multidisciplinary experts from academia, the federal government, industry, and patient advocates to develop NET clinical trials in the era of PRRT. Key clinical trial recommendations for development included 1) PRRT re-treatment, 2) PRRT and immunotherapy combinations, 3) PRRT and DNA damage repair inhibitor combinations, 4) treatment for liver-dominant disease, 5) treatment for PRRT-resistant disease, and 6) dosimetry-modified PRRT.

Next-generation matrices for marine metapopulations: The case of sea lice on salmon farms.

Classifying habitat patches as sources or sinks and determining metapopulation persistence requires coupling connectivity between habitat patches with local demographic rates. While methods to calculate sources, sinks, and metapopulation persistence exist for discrete-time models, there is no method that is consistent across modeling frameworks. In this paper, we show how next-generation matrices, originally popularized in epidemiology to calculate new infections after one generation, can be used in an ecological context to calculate sources and sinks as well as metapopulation persistence in marine metapopulations. To demonstrate the utility of the method, we construct a next-generation matrix for a network of sea lice populations on salmon farms in the Broughton Archipelago, BC, an intensive salmon farming region on the west coast of Canada where certain salmon farms are currently being removed under an agreement between local First Nations and the provincial government. The column sums of the next-generation matrix can determine if a habitat patch is a source or a sink and the spectral radius of the next-generation matrix can determine the persistence of the metapopulation. With respect to salmon farms in the Broughton Archipelago, we identify the salmon farms which are acting as the largest sources of sea lice and show that in this region the most productive sea lice populations are also the most connected. The farms which are the largest sources of sea lice have not yet been removed from the Broughton Archipelago, and warming temperatures could lead to increased sea louse growth. Calculating sources, sinks, and persistence in marine metapopulations using the next-generation matrix is biologically intuitive, mathematically equivalent to previous methods, and consistent across different modeling frameworks.

COVID-19-Related Employment Disruptions and Increased Financial Burden Among Survivors of Adolescent and Young Adult Cancer.

Background: Financial burden is a major concern for survivors of adolescent and young adult (AYA) cancers. We identified if employment disruptions during the COVID-19 pandemic affected AYA survivors' financial burden. Methods: AYAs who were enrolled in a cancer patient navigation program were e-mailed a survey in fall 2020. Survey items included sociodemographics, employment disruption, and two measures of financial burden: COmprehensive Score for Financial Toxicity (COST) and material and behavioral financial hardship items (for any reason, COVID-19 induced, cancer induced). Financial burden outcomes were dichotomized at the median (COST = 21; financial hardship = 3). The association of employment disruptions and sociodemographics with financial burden was assessed using multivariable logistic regression models. Results: Reduced hours/job loss was reported by 24.0% of 341 participants. Survivors with a high school education or less (odds ratio [OR]: 2.70; 95% confidence interval [CI]: 1.21-6.03) or who had decreased hours or job loss (OR: 3.97; 95% CI: 2.01-7.84) had greater odds for high financial toxicity. Reduced hours/job loss was the only factor associated with high material and behavioral financial hardship for both any reason (OR: 2.75; 95% CI: 1.41-5.33) and owing to COVID-19 (OR: 4.98; 95% CI: 2.28-10.92). Cancer treatment since March 2020 was associated with cancer-induced high material and behavioral financial hardship (OR: 3.31; 95% CI: 1.96-5.58). Conclusion: Employment disruptions owing to the COVID-19 pandemic, lower education levels, and cancer treatment were associated with high financial burden among AYA cancer survivors. Our findings suggest the need for multilevel interventions to identify and address financial burden among vulnerable cancer survivors.

Timing and probability of arrival for sea lice dispersing between salmon farms.

Sea lice are a threat to the health of both wild and farmed salmon and an economic burden for salmon farms. With a free-living larval stage, sea lice can disperse tens of kilometres in the ocean between salmon farms, leading to connected sea louse populations that are difficult to control in isolation. In this paper, we develop a simple analytical model for the dispersal of sea lice (Lepeophtheirus salmonis) between two salmon farms. From the model, we calculate the arrival time distribution of sea lice dispersing between farms, as well as the level of cross-infection of sea lice. We also use numerical flows from a hydrodynamic model, coupled with a particle tracking model, to directly calculate the arrival time of sea lice dispersing between two farms in the Broughton Archipelago, British Columbia, in order to fit our analytical model and find realistic parameter estimates. Using the parametrized analytical model, we show that there is often an intermediate interfarm spacing that maximizes the level of cross-infection between farms, and that increased temperatures will lead to increased levels of cross-infection.

Sources of informal financial support among adolescent and young adult cancer survivors: a mixed methods analysis from the HIAYA CHAT study.

PURPOSE: The purpose of this exploratory sequential mixed methods study was to describe the sources of informal financial support used by adolescent and young adult (AYA) cancer survivors and how financial toxicity and demographic factors were associated with different types and magnitudes of informal financial support. METHODS: This analysis is part of a larger health insurance literacy study that included pre-trial interviews and a randomized controlled trial (RCT) for AYA cancer survivors. Eligible study participants were 18 years of age, diagnosed with cancer as an AYA (15-39 years), insured, and for the RCT sample less than 1 year from diagnosis. Interview audio was transcribed, quality checked, and thematically analyzed. RCT baseline and follow-up surveys captured informal financial support use. Chi-squared and Fisher's exact tests were used to assess differences in informal financial support type use and frequency by financial toxicity and AYA demographics. RESULTS: A total of N = 24 and N = 86 AYAs participated in pre-trial interviews and the RCT respectively. Interview participants reported a variety of informal financial support sources including savings, community, family/friends, and fundraisers. However, only half of participants reported their informal financial support to be sufficient. High financial toxicity was associated with the most types of informal financial support and a higher magnitude of use. The lowest income group accessed informal financial supports less frequently than higher income groups. CONCLUSION: Our study demonstrates that AYA survivors experiencing financial toxicity frequently turn to informal sources of financial support and the magnitude is associated with financial toxicity. However, low-income survivors, and other at-risk survivors, may not have access to informal sources of financial support potentially widening inequities.

Two distinct rotations of bithiazole DNA intercalation revealed by direct comparison of crystal structures of Co(III)•bleomycin A2 and B2 bound to duplex 5'-TAGTT sites.

Bleomycins constitute a family of anticancer natural products that bind DNA through intercalation of a C-terminal tail/bithiazole moiety and hydrogen-bonding interactions between the remainder of the drug and the minor groove. The clinical utility of the bleomycins is believed to result from single- and double-strand DNA cleavage mediated by the HOO-Fe(III) form of the drug. The bleomycins also serve as a model system to understand the nature of complex drug-DNA interactions that may guide future DNA-targeted drug discovery. In this study, the impact of the C-terminal tail on bleomycin-DNA interactions was investigated. Toward this goal, we determined two crystal structures of HOO-Co(III)•BLMA2 "green" (a stable structural analogue of the active HOO-Fe(III) drug) bound to duplex DNA containing 5'-TAGTT, one in which the entire drug is bound (fully bound) and a second with only the C-terminal tail/bithiazole bound (partially bound). The structures reported here were captured by soaking HOO-Co(III)•BLMA2 into preformed host-guest crystals including a preferred DNA-binding site. While the overall structure of DNA-bound BLMA2 was found to be similar to those reported earlier at the same DNA site for BLMB2, the intercalated bithiazole of BLMB2 is "flipped" 180˚ relative to DNA-bound BLMA2. This finding highlights an unidentified role for the C-terminal tail in directing the intercalation of the bithiazole. In addition, these analyses identified specific bond rotations within the C-terminal domain of the drug that may be relevant for its reorganization and ability to carry out a double-strand DNA cleavage event.

Detecting minimum energy states and multi-stability in nonlocal advection-diffusion models for interacting species.

Deriving emergent patterns from models of biological processes is a core concern of mathematical biology. In the context of partial differential equations, these emergent patterns sometimes appear as local minimisers of a corresponding energy functional. Here we give methods for determining the qualitative structure of local minimum energy states of a broad class of multi-species nonlocal advection-diffusion models, recently proposed for modelling the spatial structure of ecosystems. We show that when each pair of species respond to one another in a symmetric fashion (i.e. via mutual avoidance or mutual attraction, with equal strength), the system admits an energy functional that decreases in time and is bounded below. This suggests that the system will eventually reach a local minimum energy steady state, rather than fluctuating in perpetuity. We leverage this energy functional to develop tools, including a novel application of computational algebraic geometry, for making conjectures about the number and qualitative structure of local minimum energy solutions. These conjectures give a guide as to where to look for numerical steady state solutions, which we verify through numerical analysis. Our technique shows that even with two species, multi-stability with up to four classes of local minimum energy states can emerge. The associated dynamics include spatial sorting via aggregation and repulsion both within and between species. The emerging spatial patterns include a mixture of territory-like segregation as well as narrow spike-type solutions. Overall, our study reveals a general picture of rich multi-stability in systems of moving and interacting species.