1,715 research outputs found
An Exact Algorithm for Side-Chain Placement in Protein Design
Computational protein design aims at constructing novel or improved functions
on the structure of a given protein backbone and has important applications in
the pharmaceutical and biotechnical industry. The underlying combinatorial
side-chain placement problem consists of choosing a side-chain placement for
each residue position such that the resulting overall energy is minimum. The
choice of the side-chain then also determines the amino acid for this position.
Many algorithms for this NP-hard problem have been proposed in the context of
homology modeling, which, however, reach their limits when faced with large
protein design instances.
In this paper, we propose a new exact method for the side-chain placement
problem that works well even for large instance sizes as they appear in protein
design. Our main contribution is a dedicated branch-and-bound algorithm that
combines tight upper and lower bounds resulting from a novel Lagrangian
relaxation approach for side-chain placement. Our experimental results show
that our method outperforms alternative state-of-the art exact approaches and
makes it possible to optimally solve large protein design instances routinely
Periodic mass loss episodes due to an oscillation mode with variable amplitude in the hot supergiant HD50064
We aim to interpret the photometric and spectroscopic variability of the
luminous blue variable supergiant HD\,50064 ().CoRoT space photometry
and follow-up high-resolution spectroscopy, with a time base of 137\,d and
169\,d, respectively, was gathered, analysed and interpreted using standard
time series analysis and light curve modelling methods as well as spectral line
diagnostics.The space photometry reveals one period of 37\,d, which undergoes a
sudden amplitude change with a factor 1.6. The pulsation period is confirmed in
the spectroscopy, which additionally reveals metal line radial velocity values
differing by km\,s depending on the spectral line and on the
epoch. We estimate \teff13\,500\,K, \logg1.5 from the equivalent
width of Si lines. The Balmer lines reveal that the star undergoes episodes of
changing mass loss on a time scale similar to the changes in the photometric
and spectroscopic variability, with an average value of (in M\,yr). We tentatively interpret the 37\,d
period as due to a strange mode oscillation.Comment: 4 pages, accepted for publication in Astronomy & Astrophysics Letter
Does segmentation always improve model performance in credit scoring?
Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache
Line-profile variations of stochastically excited oscillations in four evolved stars
Since solar-like oscillations were first detected in red-giant stars, the
presence of non-radial oscillation modes has been debated. Spectroscopic
line-profile analysis was used in the first attempt to perform mode
identification, which revealed that non-radial modes are observable. Despite
the fact that the presence of non-radial modes could be confirmed, the degree
or azimuthal order could not be uniquely identified. Here we present an
improvement to this first spectroscopic line-profile analysis. Aims: We aim to
study line-profile variations of stochastically excited solar-like oscillations
in four evolved stars to derive the azimuthal order of the observed mode and
the surface rotational frequency. Methods: Spectroscopic line-profile analysis
is applied to cross-correlation functions, using the Fourier Parameter Fit
method on the amplitude and phase distributions across the profiles. Results:
For four evolved stars, beta Hydri (G2IV), epsilon Ophiuchi (G9.5III), eta
Serpentis (K0III) and delta Eridani (K0IV) the line-profile variations reveal
the azimuthal order of the oscillations with an accuracy of ~1. Furthermore,
our analysis reveals the projected rotational velocity and the inclination
angle. From these parameters we obtain the surface rotational frequency.
Conclusions: We conclude that line-profile variations of cross-correlation
functions behave differently for different frequencies and that they provide
additional information in terms of the surface rotational frequency and
azimuthal order.Comment: Accepted for publication in Astronomy and Astrophysics, 9 pages, 10
figures and 3 tables. A version with figure 1 in full resolution can be
obtained upon request from first autho
Is the uptake, engagement, and effectiveness of exclusively mobile interventions for the promotion of weight-related behaviors equal for all? A systematic review
Mobile health interventions are promising behavior change tools. However, there is a concern that they may benefit some populations less than others and thus widen inequalities in health. This systematic review investigated differences in uptake of, engagement with, and effectiveness of mobile interventions for weight-related behaviors (i.e., diet, physical activity, and sedentary behavior) based on a range of inequality indicators including age, gender, race/ethnicity, and socioeconomic status. The protocol was registered on PROSPERO (CRD42020192473). Six databases (CINAHL, EMBASE, ProQuest, PsycINFO, Pubmed, and Web of Science) were searched from inception to July 2021. Publications were eligible for inclusion if they reported the results of an exclusively mobile intervention and examined outcomes by at least one inequality indicator. Sixteen publications reporting on 13 studies were included with most reporting on multiple behaviors and inequality indicators. Uptake was investigated in one study with no differences reported by the inequality indicators studied. Studies investigating engagement (n = 7) reported differences by age (n = 1), gender (n = 3), ethnicity (n = 2), and education (n = 2), while those investigating effectiveness (n = 9) reported differences by age (n = 3), gender (n = 5), education (n = 2), occupation (n = 1), and geographical location (n = 1). Given the limited number of studies and their inconsistent findings, evidence of the presence of a digital divide in mobile interventions targeting weight-related behaviors is inconclusive. Therefore, we recommend that inequality indicators are specifically addressed, analyzed, and reported when evaluating mobile interventions
Taking the customer into account in collaborative design
This article describes the improvement of a model of collaborative design for the ceramic industry. A new stakeholder playing a crucial role is now included in the design process, i.e. the customer. Specifically, we present a pilot validation study for the framework that aims to analyse how the environment, experiences and reference criteria of different types of the customers (commercial dealers, final users, architects and interior designers, etc.) can affect their preferences. Information about these customer preferences could be very useful for designers during the early stages of product development. A multidisciplinary approach to the problem can introduce substantial improvements in defining a truly collaborative design chain
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
Dimensionality reduction methods for count data are critical to a wide range
of applications in medical informatics and other fields where model
interpretability is paramount. For such data, hierarchical Poisson matrix
factorization (HPF) and other sparse probabilistic non-negative matrix
factorization (NMF) methods are considered to be interpretable generative
models. They consist of sparse transformations for decoding their learned
representations into predictions. However, sparsity in representation decoding
does not necessarily imply sparsity in the encoding of representations from the
original data features. HPF is often incorrectly interpreted in the literature
as if it possesses encoder sparsity. The distinction between decoder sparsity
and encoder sparsity is subtle but important. Due to the lack of encoder
sparsity, HPF does not possess the column-clustering property of classical NMF
-- the factor loading matrix does not sufficiently define how each factor is
formed from the original features. We address this deficiency by
self-consistently enforcing encoder sparsity, using a generalized additive
model (GAM), thereby allowing one to relate each representation coordinate to a
subset of the original data features. In doing so, the method also gains the
ability to perform feature selection. We demonstrate our method on simulated
data and give an example of how encoder sparsity is of practical use in a
concrete application of representing inpatient comorbidities in Medicare
patients.Comment: Fixed typo in Eq
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