Flip Distance Between Triangulations of a Simple Polygon is NP-Complete

Let T be a triangulation of a simple polygon. A flip in T is the operation of removing one diagonal of T and adding a different one such that the resulting graph is again a triangulation. The flip distance between two triangulations is the smallest number of flips required to transform one triangulation into the other. For the special case of convex polygons, the problem of determining the shortest flip distance between two triangulations is equivalent to determining the rotation distance between two binary trees, a central problem which is still open after over 25 years of intensive study. We show that computing the flip distance between two triangulations of a simple polygon is NP-complete. This complements a recent result that shows APX-hardness of determining the flip distance between two triangulations of a planar point set.Comment: Accepted versio

Site-Directed Mutations in Tyrosine 195 of Cyclodextrin Glycosyltransferase from Bacillus circulans Strain 251 Affect Activity and Product Specificity

Tyrosine 195 is located in the center of the active site cleft of cyclodextrin glycosyltransferase (EC 2.4.1.19) from Bacillus circulans strain 251. Alignment of amino acid sequences of CGTases and alpha-amylases, and the analysis of the binding mode of the substrate analogue acarbose in the active site cleft [Strokopytov, B., et al. (1995) Biochemistry 34, (in press)], suggested that Tyr195 plays an important role in cyclization of oligosaccharides. Tyr195 therefore was replaced with Phe (Y195F), Trp (Y195W), Leu (Y195L), and Gly (Y195G). Mutant proteins were purified and crystallized, and their X-ray structures were determined at 2.5-2.6 Angstrom resolution, allowing a detailed comparison of their biochemical properties and three-dimensional structures with those of the wild-type CGTase protein. The mutant proteins possessed significantly reduced cyclodextrin forming and coupling activities but were not negatively affected in the disproportionation and saccharifying reactions. Also under production process conditions, after a 45 h incubation with a 10% starch solution, the Y195W, Y195L, and Y195G mutants showed a lower overall conversion of starch into cyclodextrins. These mutants produced a considerable amount of linear maltooligosaccharides. The presence of aromatic amino acids (Tyr or Phe) at the Tyr195 position thus appears to be of crucial importance for an efficient cyclization reaction, virtually preventing the formation of linear products. Mass spectrometry of the Y195L reaction mixture, but not that of the other mutants and the wild type, revealed a shift toward the synthesis (in low yields) of larger products, especially of beta- and gamma- (but no alpha-) cyclodextrins and minor amounts of delta-, epsilon-, zeta- and eta-cyclodextrins. This again points to an important role for the residue at position 195 in the formation of cyclic products

On the Performance Prediction of BLAS-based Tensor Contractions

Tensor operations are surging as the computational building blocks for a variety of scientific simulations and the development of high-performance kernels for such operations is known to be a challenging task. While for operations on one- and two-dimensional tensors there exist standardized interfaces and highly-optimized libraries (BLAS), for higher dimensional tensors neither standards nor highly-tuned implementations exist yet. In this paper, we consider contractions between two tensors of arbitrary dimensionality and take on the challenge of generating high-performance implementations by resorting to sequences of BLAS kernels. The approach consists in breaking the contraction down into operations that only involve matrices or vectors. Since in general there are many alternative ways of decomposing a contraction, we are able to methodically derive a large family of algorithms. The main contribution of this paper is a systematic methodology to accurately identify the fastest algorithms in the bunch, without executing them. The goal is instead accomplished with the help of a set of cache-aware micro-benchmarks for the underlying BLAS kernels. The predictions we construct from such benchmarks allow us to reliably single out the best-performing algorithms in a tiny fraction of the time taken by the direct execution of the algorithms.Comment: Submitted to PMBS1

Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance

In this paper we present a linear regression model for modal symbolic data. The observed variables are histogram variables according to the definition given in the framework of Symbolic Data Analysis and the parameters of the model are estimated using the classic Least Squares method. An appropriate metric is introduced in order to measure the error between the observed and the predicted distributions. In particular, the Wasserstein distance is proposed. Some properties of such metric are exploited to predict the response variable as direct linear combination of other independent histogram variables. Measures of goodness of fit are discussed. An application on real data corroborates the proposed method

Enhancing Resource Management through Prediction-based Policies

Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly scheduling application tasks to cores. Additionally, these runtime systems offer policies to cope with application phases that lack in parallelism to fill all cores. However, these policies are usually static and favor either performance or energy efficiency. In this paper, we have extended a task-based runtime system with a lightweight monitoring and prediction infrastructure that dynamically predicts the optimal number of cores required for each application phase, thus improving both performance and energy efficiency. Through the execution of several benchmarks in multi-/many-core systems, we show that our prediction-based policies have competitive performance while improving energy efficiency when compared to state of the art policies.Comment: Postprint submitted and published at Euro-Par2020: International European Conference on Parallel and Distributed Computing (Springer) (https://link.springer.com/chapter/10.1007%2F978-3-030-57675-2_31

Depression is associated with enhanced aversive Pavlovian control over instrumental behaviour.

The dynamic modulation of instrumental behaviour by conditioned Pavlovian cues is an important process in decision-making. Patients with major depressive disorder (MDD) are known to exhibit mood-congruent biases in information processing, which may occur due to Pavlovian influences, but this hypothesis has never been tested directly in an unmedicated sample. To address this we tested unmedicated MDD patients and healthy volunteers on a computerized Pavlovian-Instrumental Transfer (PIT) task designed to separately examine instrumental approach and withdrawal actions in the context of Pavlovian appetitive and aversive cues. This design allowed us to directly measure the degree to which Pavlovian cues influence instrumental responding. Depressed patients were profoundly influenced by aversive Pavlovian stimuli, to a significantly greater degree than healthy volunteers. This was the case for instrumental behaviour both in the approach condition (in which aversive Pavlovian cues inhibited 'go' responses), and in the withdrawal condition (in which aversive Pavlovian cues facilitated 'go' responses). Exaggerated aversive PIT provides a potential cognitive mechanism for biased emotion processing in major depression. This finding also has wider significance for the understanding of disrupted motivational processing in neuropsychiatric disorders.This work was supported by a Medical Research Council project grant (G0901275) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre

The Effect of Epstein-Barr Virus Latent Membrane Protein 2 Expression on the Kinetics of Early B Cell Infection

Infection of human B cells with wild-type Epstein-Barr virus (EBV) in vitro leads to activation and proliferation that result in efficient production of lymphoblastoid cell lines (LCLs). Latent Membrane Protein 2 (LMP2) is expressed early after infection and previous research has suggested a possible role in this process. Therefore, we generated recombinant EBV with knockouts of either or both protein isoforms, LMP2A and LMP2B (Δ2A, Δ2B, Δ2A/Δ2B) to study the effect of LMP2 in early B cell infection. Infection of B cells with Δ2A and Δ2A/Δ2B viruses led to a marked decrease in activation and proliferation relative to wild-type (wt) viruses, and resulted in higher percentages of apoptotic B cells. Δ2B virus infection showed activation levels comparable to wt, but fewer numbers of proliferating B cells. Early B cell infection with wt, Δ2A and Δ2B viruses did not result in changes in latent gene expression, with the exception of elevated LMP2B transcript in Δ2A virus infection. Infection with Δ2A and Δ2B viruses did not affect viral latency, determined by changes in LMP1/Zebra expression following BCR stimulation. However, BCR stimulation of Δ2A/Δ2B cells resulted in decreased LMP1 expression, which suggests loss of stability in viral latency. Long-term outgrowth assays revealed that LMP2A, but not LMP2B, is critical for efficient long-term growth of B cells in vitro. The lowest levels of activation, proliferation, and LCL formation were observed when both isoforms were deleted. These results suggest that LMP2A appears to be critical for efficient activation, proliferation and survival of EBV-infected B cells at early times after infection, which impacts the efficient long-term growth of B cells in culture. In contrast, LMP2B did not appear to play a significant role in these processes, and long-term growth of infected B cells was not affected by the absence of this protein. © 2013 Wasil et al

Bioenergetic model sensitivity to diet diversity across space, time and ontogeny

Consumption is the primary trophic interaction in ecosystems and its accurate estimation is required for reliable ecosystem modeling. When estimating consumption, species' diets are commonly assumed to be the average of those that occur among habitats, seasons, and life stages which introduces uncertainty and error into consumption rate estimates. We present a case study of a teleost (Yellowfin Bream Acanthopagrus australis) that quantifies the potential error in consumption (in mass) and growth rate estimates when using diet data from different regions and times and ignoring ontogenetic variability. Ontogenetic diet trends were examined through gut content analysis (n = 1,130 fish) and incorporated into a bioenergetic model (the "primary " model) that included diet variability (n = 144 prey sources) and ontogenetic changes in metabolism (1-7 year) to estimate lifetime consumption. We quantified error by building nine model scenarios that each incorporated different spatiotemporal diet data of four published studies. The model scenarios produced individual lifetime consumption estimates that were between 25% lower and 15% higher than the primary model (maximum difference was 53%, range 11.7-17.8 kg). When consumption (in mass) was held constant, differences in diet quality among models caused a several-fold range in growth rate (0.04-1.07 g day(-1)). Our findings showcase the large uncertainty in consumption rate estimates due to diet diversity, and illustrate that caution is required when considering bioenergetic results among locations, times, and ontogeny

COVID-19 vaccination uptake amongst ethnic minority communities in England: a linked study exploring the drivers of differential vaccination rates

BACKGROUND: Despite generally high coronavirus disease 2019 (COVID-19) vaccination rates in the UK, vaccination hesitancy and lower take-up rates have been reported in certain ethnic minority communities. METHODS: We used vaccination data from the National Immunisation Management System (NIMS) linked to the 2011 Census and individual health records for subjects aged ≥40 years (n = 24 094 186). We estimated age-standardized vaccination rates, stratified by ethnic group and key sociodemographic characteristics, such as religious affiliation, deprivation, educational attainment, geography, living conditions, country of birth, language skills and health status. To understand the association of ethnicity with lower vaccination rates, we conducted a logistic regression model adjusting for differences in geographic, sociodemographic and health characteristics. ResultsAll ethnic groups had lower age-standardized rates of vaccination compared with the white British population, whose vaccination rate of at least one dose was 94% (95% CI: 94%-94%). Black communities had the lowest rates, with 75% (74-75%) of black African and 66% (66-67%) of black Caribbean individuals having received at least one dose. The drivers of these lower rates were partly explained by accounting for sociodemographic differences. However, modelled estimates showed significant differences remained for all minority ethnic groups, compared with white British individuals. CONCLUSIONS: Lower COVID-19 vaccination rates are consistently observed amongst all ethnic minorities

Obesity, Ethnicity, and Risk of Critical Care, Mechanical Ventilation, and Mortality in Patients Admitted to Hospital with COVID-19: Analysis of the ISARIC CCP-UK Cohort

OBJECTIVE: The aim of this study was to investigate the association of obesity with in-hospital coronavirus disease 2019 (COVID-19) outcomes in different ethnic groups. METHODS: Patients admitted to hospital with COVID-19 in the United Kingdom through the Clinical Characterisation Protocol UK (CCP-UK) developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) were included from February 6 to October 12, 2020. Ethnicity was classified as White, South Asian, Black, and other minority ethnic groups. Outcomes were admission to critical care, mechanical ventilation, and in-hospital mortality, adjusted for age, sex, and chronic diseases. RESULTS: Of the participants included, 54,254 (age = 76 years; 45.0% women) were White, 3,728 (57 years; 41.1% women) were South Asian, 2,523 (58 years; 44.9% women) were Black, and 5,427 (61 years; 40.8% women) were other ethnicities. Obesity was associated with all outcomes in all ethnic groups, with associations strongest for black ethnicities. When stratified by ethnicity and obesity status, the odds ratios for admission to critical care, mechanical ventilation, and mortality in black ethnicities with obesity were 3.91 (3.13-4.88), 5.03 (3.94-6.63), and 1.93 (1.49-2.51), respectively, compared with White ethnicities without obesity. CONCLUSIONS: Obesity was associated with an elevated risk of in-hospital COVID-19 outcomes in all ethnic groups, with associations strongest in Black ethnicities