Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

Evidence for early life in Earth’s oldest hydrothermal vent precipitates

Although it is not known when or where life on Earth began, some of the earliest habitable environments may have been submarine-hydrothermal vents. Here we describe putative fossilized microorganisms that are at least 3,770 million and possibly 4,280 million years old in ferruginous sedimentary rocks, interpreted as seafloor-hydrothermal vent-related precipitates, from the Nuvvuagittuq belt in Quebec, Canada. These structures occur as micrometre-scale haematite tubes and filaments with morphologies and mineral assemblages similar to those of filamentous microorganisms from modern hydrothermal vent precipitates and analogous microfossils in younger rocks. The Nuvvuagittuq rocks contain isotopically light carbon in carbonate and carbonaceous material, which occurs as graphitic inclusions in diagenetic carbonate rosettes, apatite blades intergrown among carbonate rosettes and magnetite–haematite granules, and is associated with carbonate in direct contact with the putative microfossils. Collectively, these observations are consistent with an oxidized biomass and provide evidence for biological activity in submarine-hydrothermal environments more than 3,770 million years ago

Central carbon metabolism in the progression of mammary carcinoma

There is a growing belief that the metabolic program of breast tumor cells could be a therapeutic target. Yet, without detailed information on central carbon metabolism in breast tumors it is impossible to know which metabolic pathways to target, and how their inhibition might influence different stages of breast tumor progression. Here we perform the first comprehensive profiling of central metabolism in the MCF10 model of mammary carcinoma, where the steps of breast tumor progression (transformation, tumorigenicity and metastasis) can all be examined in the context of the same genetic background. The metabolism of [U-13C]-glucose by a series of progressively more aggressive MCF10 cell lines was tracked by 2D NMR and mass spectrometry. From this analysis the flux of carbon through distinct metabolic reactions was quantified by isotopomer modeling. The results indicate widespread changes to central metabolism upon cellular transformation including increased carbon flux through the pentose phosphate pathway (PPP), the TCA cycle, as well as increased synthesis of glutamate, glutathione and fatty acids (including elongation and desaturation). The de novo synthesis of glycine increased upon transformation as well as at each subsequent step of breast tumor cell progression. Interestingly, the major metabolic shift in metastatic cells is a large increase in the de novo synthesis of proline. This work provides the first comprehensive view of changes to central metabolism as a result of breast tumor progression

Artificial Intelligence in Education

Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

An evidence-based framework for predicting the impact of differing autotroph-heterotroph thermal sensitivities on consumer-prey dynamics

Increased temperature accelerates vital rates, influencing microbial population and wider ecosystem dynamics, for example, the predicted increases in cyanobacterial blooms associated with global warming. However, heterotrophic and mixotrophic protists, which are dominant grazers of microalgae, may be more thermally sensitive than autotrophs, and thus prey could be suppressed as temperature rises. Theoretical and meta-analyses have begun to address this issue, but an appropriate framework linking experimental data with theory is lacking. Using ecophysiological data to develop a novel model structure, we provide the first validation of this thermal sensitivity hypothesis: increased temperature improves the consumer’s ability to control the autotrophic prey. Specifically, the model accounts for temperature effects on auto- and mixotrophs and ingestion, growth and mortality rates, using an ecologically and economically important system (cyanobacteria grazed by a mixotrophic flagellate). Once established, we show the model to be a good predictor of temperature impacts on consumer–prey dynamics by comparing simulations with microcosm observations. Then, through simulations, we indicate our conclusions remain valid, even with large changes in bottom-up factors (prey growth and carrying capacity). In conclusion, we show that rising temperature could, counterintuitively, reduce the propensity for microalgal blooms to occur and, critically, provide a novel model framework for needed, continued assessment

The role of GLI-SOX2 signaling axis for gemcitabine resistance in pancreatic cancer

Pancreatic cancer, mostly pancreatic ductal adenocarcinomas (PDAC), is one of the most lethal cancers, with a dismal median survival around 8 months. PDAC is notoriously resistant to chemotherapy. Thus far, numerous attempts using novel targeted therapies and immunotherapies yielded limited clinical benefits for pancreatic cancer patients. It is hoped that delineating the molecular mechanisms underlying drug resistance in pancreatic cancer may provide novel therapeutic options. Using acquired gemcitabine resistant pancreatic cell lines, we revealed an important role of the GLI-SOX2 signaling axis for regulation of gemcitabine sensitivity in vitro and in animal models. Down-regulation of GLI transcriptional factors (GLI1 or GLI2), but not SMO signaling inhibition, reduces tumor sphere formation, a characteristics of tumor initiating cell (TIC). Down-regulation of GLI transcription factors also decreased expression of TIC marker CD24. Similarly, high SOX2 expression is associated with gemcitabine resistance whereas down-regulation of SOX2 sensitizes pancreatic cancer cells to gemcitabine treatment. We further revealed that elevated SOX2 expression is associated with an increase in GLI1 or GLI2 expression. Our ChIP assay revealed that GLI proteins are associated with a putative Gli binding site within the SOX2 promoter, suggesting a more direct regulation of SOX2 by GLI transcription factors. The relevance of our findings to human disease was revealed in human cancer specimens. We found that high SOX2 protein expression is associated with frequent tumor relapse and poor survival in stage II PDAC patients (all of them underwent gemcitabine treatment), indicating that reduced SOX2 expression or down-regulation of GLI transcription factors may be effective in sensitizing pancreatic cancer cells to gemcitabine treatment

Optimism and commitment: An elementary theory of bargaining and war

We propose an elementary theory of wars fought by fully rational contenders. Two parties play a Markov game that combines stages of bargaining with stages where one side has the ability to impose surrender on the other. Under uncertainty and incomplete information, in the unique equilibrium of the game, long confrontations occur: war arises when reality disappoints initial (rational) optimism, and it persist longer when both agents are optimists but reality proves both wrong. Bargaining proposals that are rejected initially might eventually be accepted after several periods of confrontation. We provide an explicit computation of the equilibrium, evaluating the probability of war, and its expected losses as a function of i) the costs of confrontation, ii) the asymmetry of the split imposed under surrender, and iii) the strengths of contenders at attack and defense. Changes in these parameters display non-monotonic effects

Outer membrane protein folding from an energy landscape perspective

The cell envelope is essential for the survival of Gram-negative bacteria. This specialised membrane is densely packed with outer membrane proteins (OMPs), which perform a variety of functions. How OMPs fold into this crowded environment remains an open question. Here, we review current knowledge about OFMP folding mechanisms in vitro and discuss how the need to fold to a stable native state has shaped their folding energy landscapes. We also highlight the role of chaperones and the β-barrel assembly machinery (BAM) in assisting OMP folding in vivo and discuss proposed mechanisms by which this fascinating machinery may catalyse OMP folding