Exploring population responses to environmental change when there is never enough data: a factor analytic approach

© 2018 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society Temporal variability in the environment drives variation in vital rates, with consequences for population dynamics and life-history evolution. Integral projection models (IPMs) are data-driven structured population models widely used to study population dynamics and life-history evolution in temporally variable environments. However, many datasets have insufficient temporal replication for the environmental drivers of vital rates to be identified with confidence, limiting their use for evaluating population level responses to environmental change. Parameter selection, where the kernel is constructed at each time step by randomly selecting the time-varying parameters from their joint probability distribution, is one approach to including stochasticity in IPMs. We consider a factor analytic (FA) approach for modelling the covariance matrix of time-varying parameters, whereby latent variable(s) describe the covariance among vital rate parameters. This decreases the number of parameters to estimate and, where the covariance is positive, the latent variable can be interpreted as a measure of environmental quality. We demonstrate this using simulation studies and two case studies. The simulation studies suggest the FA approach provides similarly accurate estimates of stochastic population growth rate to estimating an unstructured covariance matrix. We demonstrate how the latent parameter can be perturbed to show how selection on reproductive delays in the monocarp Carduus nutans changes under different environmental conditions. We develop a demographic model of the fire dependent herb Eryngium cuneifolium to show how a putative driver of the variation in environmental quality can be incorporated with the addition of a single parameter. Using perturbation analyses we determine optimal management strategies for this species. This approach estimates fewer parameters than previous approaches and allows novel eco-evolutionary insights. Predictions on population dynamics and life-history evolution under different environmental conditions can be made without necessarily identifying causal factors. Putative environmental drivers can be incorporated with relatively few parameters, allowing for predictions on how populations will respond to changes in the environment

The implications of seasonal climatic effects for managing disturbance dependent populations under a changing climate

The frequency of ecological disturbances, such as fires, is changing due to changing land use and climatic conditions. Disturbance-adapted species may thus require the manipulation of disturbance regimes to persist. However, the effects of changes in other abiotic factors, such as climatic conditions, are frequently disregarded in studies of such systems. Where climatic effects are included, relatively simple approaches that disregard seasonal variation in the effects are typically used. We compare predictions of population persistence using different fire return intervals (FRIs) under recent and predicted future climatic conditions for the rare fire-dependent herb Eryngium cuneifolium. We used functional linear models (FLMs) to estimate the cumulative effect of climatic variables across the annual cycle, allowing the strength and direction of the climatic impacts to differ over the year. We then estimated extinction probabilities and minimum population sizes under past and forecasted future climatic conditions and a range of FRIs. Under forecasted climate change, E. cuneifolium is predicted to persist under a much broader range of FRIs, because increasing temperatures are associated with faster individual growth. Climatic impacts on fecundity do not result in a temporal trend in this vital rate due to antagonistic seasonal effects operating through winter and summer temperatures. These antagonistic seasonal climatic effects highlight the importance of capturing the seasonal dependence of climatic effects when forecasting their future fate. Synthesis. Awareness of the potential effects of climate change on disturbance-adapted species is necessary for developing suitable management strategies for future environmental conditions. However, our results suggest that widely used simple methods for modelling climate impacts, that disregard seasonality in such effects, may produce misleading inferences

Fine-scale spatial variation in fitness is comparable to disturbance-induced fluctuations in a fire-adapted species

The spatial scale at which demographic performance (e.g., net reproductive output) varies can profoundly influence landscape-level population growth and persistence, and many demographically pertinent processes such as species interactions and resource acquisition vary at fine scales. We compared the magnitude of demographic variation associated with fine-scale heterogeneity (1 ha) fluctuations associated with fire disturbance. We used a spatially explicit model within an IPM modeling framework to evaluate the demographic importance of fine-scale variation. We used a measure of expected lifetime fruit production, EF, that is assumed to be proportional to lifetime fitness. Demographic differences and their effects on EF were assessed in a population of the herbaceous perennial Hypericum cumulicola (~2,600 individuals), within a patch of Florida rosemary scrub (400 × 80 m). We compared demographic variation over fine spatial scales to demographic variation between years across 6 yr after a fire. Values of EF changed by orders of magnitude over <10 m. This variation in fitness over fine spatial scales (<10 m) is commensurate to postfire changes in fitness for this fire-adapted perennial. A life table response experiment indicated that fine-scale spatial variation in vital rates, especially survival, explains as much change in EF as demographic changes caused by time-since-fire, a key driver in this system. Our findings show that environmental changes over a few tens of meters can have ecologically meaningful implications for population growth and extinction

Multiferroicity in doped hexagonal LuFeO3

The hexagonal phase of LuFeO3 is a rare example of a multiferroic material possessing a weak ferromagnetic moment, which is predicted to be switchable by an electric field. We stabilize this structure in bulk form though Mn and Sc doping, and determine the complete magnetic and crystallographic structures using neutron-scattering and magnetometry techniques. The ferroelectric P6(3)cm space group is found to be stable over a wide concentration range, ordering antiferromagnetically with Neel temperatures that smoothly increase following the ratio of c to a (c/a) lattice parameters up to 172 K, the highest found in this class of materials to date. The magnetic structure for a range of temperatures and dopings is consistent with recent studies of high quality epitaxial films of pure hexagonal LuFeO3 including a ferromagnetic moment parallel to the ferroelectric axis. We propose a mechanism by which room-temperature multiferroicity could be achieved in this class of materialsopen

Towards an integrated evaluation framework for xai: an experimental study

Increasing prevalence of opaque black-box AI has highlighted the need for explanations of their behaviours, for example, via explanation artefacts/proxy models. The current paper presents a paradigm for human-grounded experiments to evaluate the relationship between explanation fidelity, human learning performance, understanding and trust in a black-box AI by manipulating the complexity of an explanatory artefact. Decision trees were used in the current experiment as exemplar interpretable surrogate models, providing explanations approximating black-box behaviour, by means of explanation by simplification. Consistent with our hypotheses: 1) explanatory artefacts brought about better learning, while greater decision tree depths led to greater interpretability of the AI's performance and greater trust in the AI; and 2) explanatory artefacts facilitated learning and task performance even after they were withdrawn. Findings are discussed in terms of the interplay between human understanding, trust and AI system performance, highlighting the simplifying assumption of a monotonic relationship between explanation fidelity and interpretability

Concurrent inhibition of oncogenic and wild-type RAS-GTP for cancer therapy

RAS oncogenes (collectively NRAS, HRAS and especially KRAS) are among the most frequently mutated genes in cancer, with common driver mutations occurring at codons 12, 13 and 611. Small molecule inhibitors of the KRAS(G12C) oncoprotein have demonstrated clinical efficacy in patients with multiple cancer types and have led to regulatory approvals for the treatment of non-small cell lung cancer2,3. Nevertheless, KRASG12C mutations account for only around 15% of KRAS-mutated cancers4,5, and there are no approved KRAS inhibitors for the majority of patients with tumours containing other common KRAS mutations. Here we describe RMC-7977, a reversible, tri-complex RAS inhibitor with broad-spectrum activity for the active state of both mutant and wild-type KRAS, NRAS and HRAS variants (a RAS(ON) multi-selective inhibitor). Preclinically, RMC-7977 demonstrated potent activity against RAS-addicted tumours carrying various RAS genotypes, particularly against cancer models with KRAS codon 12 mutations (KRASG12X). Treatment with RMC-7977 led to tumour regression and was well tolerated in diverse RAS-addicted preclinical cancer models. Additionally, RMC-7977 inhibited the growth of KRASG12C cancer models that are resistant to KRAS(G12C) inhibitors owing to restoration of RAS pathway signalling. Thus, RAS(ON) multi-selective inhibitors can target multiple oncogenic and wild-type RAS isoforms and have the potential to treat a wide range of RAS-addicted cancers with high unmet clinical need. A related RAS(ON) multi-selective inhibitor, RMC-6236, is currently under clinical evaluation in patients with KRAS-mutant solid tumours (ClinicalTrials.gov identifier: NCT05379985).J.E. Klomp is funded by National Cancer Institute grants T32CA009156, F32CA239328 and K99CA276700, and American Cancer Society grant PF-20-069. P.L. is supported in part by the NIH/NCI (1R01CA23074501, 1R01CA23026701A1 and 1R01CA279264-01), The Pew Charitable Trusts, the Damon Runyon Cancer Research Foundation, and the Pershing Square Sohn Cancer Research Alliance. P.L. is also supported by the Josie Robertson Investigator Program and the Support Grant-Core Grant program (P30 CA008748) at Memorial Sloan Kettering Cancer Center. D.S. is funded by AECC Excellence Program 2022 (EPAEC222641CICS). A.J.A. has research funding from Bristol Myers Squibb, Deerfield, Eli Lilly, Mirati Therapeutics, Novartis, Novo Ventures, Revolution Medicines and Syros Pharmaceuticals. A.M.W. was supported by a grant from the NCI (K22CA276632-01). C.J.D. has received research funding support from Deciphera Pharmaceuticals, Mirati Therapeutics, Reactive Biosciences, Revolution Medicines, and SpringWorks Therapeutics, the National Cancer Institute (P50CA257911 and R35CA232113), Department of Defense (W81XWH2110692), and Pancreatic Cancer Action Network (22-WG-DERB). C.A. is funded by grants from the Giovanni Armenise–Harvard Foundation, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 101001288) and AIRC under IG 2021–ID. 25737 project.Peer reviewe

Adventures in Decoding Direct Sum Codes

Our adventures take us through several problems related to decoding a class of error-correcting codes known as direct sum codes. These codes are obtained from a base code $\mathcal{C}_0 \subseteq \mathbb{F}_2^n$ by summing the entries of each codeword on a collection of subsets $W \subseteq [n]^k$ to yield a code $\mathcal{C} \subseteq \mathbb{F}_2^{W}$. If the collection $W$ has a particular expansion property, the resulting direct sum code $\mathcal{C}$ will have a large minimum distance and (as we show) an efficient decoding algorithm. In our first adventure (Chapter 2, joint work with Vedat Levi Alev, Fernando Granha Jeronimo, Shashank Srivastava, and Madhur Tulsiani), we develop a list decoding algorithm for a generalization of direct sum codes where the direct sum can be replaced by any suitable ``lifting'' operation. The algorithm creates a Sum-of-Squares program for list decoding, the solution to which allows the desired list of codewords to be recovered through rounding. We apply the general decoding framework to obtain list decoding algorithms for direct sum codes constructed using a collection $W$ which is either the set of walks of $k$ vertices on an expander graph or the set of $k$-sized faces of a high-dimensional expander. Our second adventure (Chapter 3, joint work with Fernando Granha Jeronimo, Shashank Srivastava, and Madhur Tulsiani) is adapting the results of Chapter 2 to decode Ta-Shma's [STOC 2017] direct sum codes. These codes are $\varepsilon$-balanced, meaning any two codewords have distance between $1/2-\varepsilon/2$ and $1/2+\varepsilon/2$, and have rate nearly achieving the Gilbert--Varshamov bound of $\Omega(\varepsilon^2)$. Decoding is accomplished using the direct sum list decoding framework in an inductive process that sidesteps its suboptimal parameter requirements. This process can perform both unique and list decoding for Ta-Shma's codes, though at a list decoding radius smaller than the minimum distance

Assessing the Structural Validity of the Knee Injury and Osteoarthritis Outcome Score Scale

Background: The Knee Injury and Osteoarthritis Outcome Score (KOOS) scale is used to assess patient perspectives on knee health. However, the structural validity of the KOOS has not been sufficiently tested; therefore, our objective was to assess the KOOS in a large, multi-site database of patient responses who were receiving care for knee pathology. Methods: A cross-sectional study was conducted using the Surgical Outcome System (SOS) database. A confirmatory factor analysis (CFA) was conducted to assess the proposed five-factor KOOS using a priori cut-off values. Because model fit indices were not met, a subsequent exploratory factor analysis (EFA) was conducted to identify a parsimonious model. The resulting four-factor structure (i.e., KOOS SF-12) was then assessed using CFA and subjected to multigroup invariance testing. Results: The original KOOS model did not meet rigorous CFA fit recommendations. The KOOS SF-12 did meet model fit recommendations and passed all invariance testing between intervention procedure, sex, and age groups. Conclusion: The KOOS failed to meet model fit recommendations. The KOOS SF-12 met model fit recommendations, maintained a multi-factorial structure, and was invariant across all tested groups. The KOOS did not demonstrate sound structural validity. A refined KOOS SF-12 model that met recommended model fit indices and invariance testing criteria was identified. Our findings provide initial support for a multidimensional KOOS structure (i.e., KOOS SF-12) that is a more psychometrically sound instrument for measuring patient-reported knee health