1,047 research outputs found
Variational Inequalities in Critical-State Problems
Similar evolutionary variational inequalities appear as convenient
formulations for continuous quasistationary models for sandpile growth,
formation of a network of lakes and rivers, magnetization of type-II
superconductors, and elastoplastic deformations. We outline the main steps of
such models derivation and try to clarify the origin of this similarity. New
dual variational formulations, analogous to mixed variational inequalities in
plasticity, are derived for sandpiles and superconductors.Comment: Submitted for publicatio
Tumor and circulating biomarkers in patients with second-line hepatocellular carcinoma from the randomized phase II study with tivantinib
ARQ 197-215 was a randomized placebo-controlled phase II study testing the MET inhibitor tivantinib in second-line hepatocellular carcinoma (HCC) patients. It identified tumor MET as a key biomarker in HCC.
Aim of this research was to study the prognostic and predictive value of tumor (MET, the receptor tyrosine kinase encoded by the homonymous MNNG-HOS transforming gene) and circulating (MET, hepatocyte growth factor [HGF], alpha-fetoprotein [AFP], vascular endothelial growth factor [VEGF]) biomarkers in second-line HCC. Tumor MET-High status was centrally assessed by immunohistochemistry. Circulating biomarkers were centrally analyzed on serum samples collected at baseline and every 4-8 weeks, using medians as cut-off to determine High/Low status. Tumor MET, tested in 77 patients, was more frequently High after (82%) versus before (40%) sorafenib. A significant interaction (p = 0.04) between tivantinib and baseline tumor MET in terms of survival was observed. Baseline circulating MET and HGF (102 patients) High status correlated with shorter survival (HR 0.61, p = 0.03, and HR 0.60, p = 0.02, respectively), while the association between AFP (104 patients) or VEGF (103 patients) status and survival was non-significant.
Conclusions: Tumor MET levels were higher in patients treated with sorafenib. Circulating biomarkers such as MET and HGF may be prognostic in second-line HCC. These results need to be confirmed in larger randomized clinical trials
Escherichia coli induces apoptosis and proliferation of mammary cells
Mammary cell apoptosis and proliferation were assessed after injection of Escherichia coli into the left mammary quarters of six cows. Bacteriological analysis of foremilk samples revealed coliform infection in the injected quarters of four cows. Milk somatic cell counts increased in these quarters and peaked at 24 h after bacterial injection. Body temperature also increased, peaking at 12 h postinjection, The number of apoptotic cells was significantly higher in the mastitic tissue than in the uninfected control. Expression of Bax and interleukin-1 beta converting enzyme increased in the mastitic tissue at 24 h and 72 h postinfection, whereas Bcl-2 expression decreased at 24 h but did not differ significantly from the control at 72 h postinfection, Induction of matrix metalloproteinase-g, stromelysin-1 and urokinase-type plasminogen activator was also observed in the mastitic tissue. Moreover, cell proliferation increased in the infected tissue, These results demonstrate that Escherichia coli-induced mastitis promotes apoptosis and cell proliferation
Confinement- Deconfinement Phase Transition in Hot and Dense QCD at Large N
We conjecture that the confinement- deconfinement phase transition in QCD at
large number of colors N and N_f\ll N at T\neq 0 and \mu\neq 0 is triggered by
the drastic change in \theta behavior. The conjecture is motivated by the
holographic model of QCD where confinement -deconfinement phase transition
indeed happens precisely at the value of temperature T=T_c where \theta
dependence experiences a sudden change in behavior[1]. The conjecture is also
supported by quantum field theory arguments when the instanton calculations
(which trigger the \theta dependence) are under complete theoretical control
for T>T_c, suddenly break down immediately below T<T_c with sharp changes in
the \theta dependence. Finally, the conjecture is supported by a number of
numerical lattice results. We employ this conjecture to study confinement
-deconfinement phase transition of dense QCD at large \mu in large N limit by
analyzing the \theta dependence. We find that the confinement- deconfinement
phase transition at N_f\ll N happens at very large quark chemical potential
\mu_c\sim \sqrt{N}\Lambda_{QCD}. This result agrees with recent findings by
McLerran and Pisarski[2]. We also speculate on case when N_f\sim N.Comment: 10 pages, final version to appear in Nucl. Phys.
Scale-free memory model for multiagent reinforcement learning. Mean field approximation and rock-paper-scissors dynamics
A continuous time model for multiagent systems governed by reinforcement
learning with scale-free memory is developed. The agents are assumed to act
independently of one another in optimizing their choice of possible actions via
trial-and-error search. To gain awareness about the action value the agents
accumulate in their memory the rewards obtained from taking a specific action
at each moment of time. The contribution of the rewards in the past to the
agent current perception of action value is described by an integral operator
with a power-law kernel. Finally a fractional differential equation governing
the system dynamics is obtained. The agents are considered to interact with one
another implicitly via the reward of one agent depending on the choice of the
other agents. The pairwise interaction model is adopted to describe this
effect. As a specific example of systems with non-transitive interactions, a
two agent and three agent systems of the rock-paper-scissors type are analyzed
in detail, including the stability analysis and numerical simulation.
Scale-free memory is demonstrated to cause complex dynamics of the systems at
hand. In particular, it is shown that there can be simultaneously two modes of
the system instability undergoing subcritical and supercritical bifurcation,
with the latter one exhibiting anomalous oscillations with the amplitude and
period growing with time. Besides, the instability onset via this supercritical
mode may be regarded as "altruism self-organization". For the three agent
system the instability dynamics is found to be rather irregular and can be
composed of alternate fragments of oscillations different in their properties.Comment: 17 pages, 7 figur
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Using Wearable Skin Temperature Data to Advance Tracking and Characterization of the Menstrual Cycle in a Real-World Setting.
The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers
The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series
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