Importance of controlling the degree of saturation in soil compaction

n the typical conventional fill compaction, the dry density ρd and the water content w are controlled in relation to (ρd)max and wopt determined by laboratory compaction tests using a representative sample at a certain compaction energy level CEL. Although CEL and actual soil type affect significantly the values of (ρd)max and wopt, they change inevitably in a given earthwork project while CEL in the field may not match the value used in the laboratory compaction tests. Compaction control based on the stiffness of compacted soil in the field has such a drawback that the stiffness drops upon wetting more largely as the degree of saturation, Sr, of compacted soil becomes lower than the optimum degree of saturation (Sr)opt defined as Sr when (ρd)max is obtained for a given CEL. In comparison, the value of (Sr)opt and the ρd/(ρd)max vs. Sr - (Sr)opt relation of compacted soil are rather insensitive to variations in CEL and soil type, while the strength and stiffness of unsoaked and soaked compacted soil is controlled by ρd and “Sr at the end of compaction”. It is proposed to control not only w and ρd but also Sr so that Sr becomes (Sr)opt and ρd becomes large enough to ensue soil properties required in design.Fundação para a Ciência e Tecnologia (FCT)info:eu-repo/semantics/publishedVersio

Analysis of overfitting in the regularized Cox model

The Cox proportional hazards model is ubiquitous in the analysis oftime-to-event data. However, when the data dimension p is comparable to thesample size N, maximum likelihood estimates for its regression parameters areknown to be biased or break down entirely due to overfitting. This promptedthe introduction of the so-called regularized Cox model. In this paper we use the replica method from statistical physics to investigate the relationship between the true and inferred regression parameters in regularized multivariate Cox regression with L2 regularization, in the regime where both p and N are large but with ζ = p/N ∼ O(1). We thereby generalize a recent study from maximum likelihood to maximum a posteriori inference. We also establish a relationship between the optimal regularization parameter and ζ, allowing for straightforward overfitting corrections in time-to-event analysis

Interpolating the Sherrington-Kirkpatrick replica trick

The interpolation techniques have become, in the past decades, a powerful approach to lighten several properties of spin glasses within a simple mathematical framework. Intrinsically, for their construction, these schemes were naturally implemented into the cavity field technique, or its variants as the stochastic stability or the random overlap structures. However the first and most famous approach to mean field statistical mechanics with quenched disorder is the replica trick. Among the models where these methods have been used (namely, dealing with frustration and complexity), probably the best known is the Sherrington-Kirkpatrick spin glass: In this paper we are pleased to apply the interpolation scheme to the replica trick framework and test it directly to the cited paradigmatic model: interestingly this allows to obtain easily the replica-symmetric control and, synergically with the broken replica bounds, a description of the full RSB scenario, both coupled with several minor theorems. Furthermore, by treating the amount of replicas $n\in(0,1]$ as an interpolating parameter (far from its original interpretation) this can be though of as a quenching temperature close to the one introduce in off-equilibrium approaches and, within this viewpoint, the proof of the attended commutativity of the zero replica and the infinite volume limits can be obtained.Comment: This article is dedicated to David Sherrington on the occasion of his seventieth birthda

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship

Correction: Robust Bayesian fluorescence lifetime estimation, decay model selection and instrument response determination for low-intensity FLIM imaging

The images for Figs 5 and 6 are incorrectly switched. The image that appears as Fig 5 should be Fig 6, and the image that appears for Fig 6 should be Fig 5. The figure captions appear in the correct order

HER2-HER3 heterodimer quantification by FRET-FLIM and patient subclass analysis of the COIN colorectal trial

BACKGROUND: The phase 3 MRC COIN trial showed no statistically significant benefit from adding the EGFR-target cetuximab to oxaliplatin-based chemotherapy in first-line treatment of advanced colorectal cancer. This study exploits additional information on HER2-HER3 dimerization to achieve patient stratification and reveal previously hidden subgroups of patients who had differing disease progression and treatment response. METHODS: HER2-HER3 dimerization was quantified by "FLIM Histology" in primary tumor samples from 550 COIN trial patients receiving oxaliplatin and fluoropyrimidine chemotherapy +/-cetuximab. Bayesian latent class analysis (LCA) and covariate reduction was performed to analyze the effects of HER2-HER3 dimer, RAS mutation and cetuximab on progression-free survival (PFS) and overall survival (OS). All statistical tests were two-sided. RESULTS: LCA on a cohort of 398 patients revealed two patient subclasses with differing prognoses (median OS: 1624 days [95%CI=1466-1816] vs 461 [95%CI=431-504]): Class 1 (15.6%) showed a benefit from cetuximab in OS (HR = 0.43 [95%CI=0.25-0.76]; p = 0.004). Class 2 showed an association of increased HER2-HER3 with better OS (HR = 0.64 [95%CI=0.44-0.94]; p = 0.02). A class prediction signature was formed and tested on an independent validation cohort (N = 152) validating the prognostic utility of the dimer assay. Similar subclasses were also discovered in full trial dataset (N = 1,630) based on 10 baseline clinicopathological and genetic covariates. CONCLUSIONS: Our work suggests that the combined use of HER dimer imaging and conventional mutation analyses will be able to identify a small subclass of patients (>10%) who will have better prognosis following chemotherapy. A larger prospective cohort will be required to confirm its utility in predicting the outcome of anti-EGFR treatment

The diminishing role of hubs in dynamical processes on complex networks

It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics as compared to intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein-protein interaction network, and a neural network, suggesting that it might indeed be a widespread property in nature.Comment: Published versio

Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development

BACKGROUND: Advanced head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis, and biomarkers that predict response to treatment are highly desirable. The primary aim was to predict progression-free survival (PFS) with a multivariate risk prediction model. METHODS: Experimental covariates were derived from blood samples of 56 HNSCC patients which were prospectively obtained within a Phase 2 clinical trial (NCT02633800) at baseline and after the first treatment cycle of combined platinum-based chemotherapy with cetuximab treatment. Clinical and experimental covariates were selected by Bayesian multivariate regression to form risk scores to predict PFS. RESULTS: A 'baseline' and a 'combined' risk prediction model were generated, each of which featuring clinical and experimental covariates. The baseline risk signature has three covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has six covariates, also featuring baseline CD33+CD14+HLADRhigh monocytes but is strongly driven by on-treatment relative change of CD8+ central memory T cells percentages. The combined model has a higher predictive power than the baseline model and was successfully validated to predict therapeutic response in an independent cohort of nine patients from an additional Phase 2 trial (NCT03494322) assessing the addition of avelumab to cetuximab treatment in HNSCC. We identified tissue counterparts for the immune cells driving the models, using imaging mass cytometry, that specifically colocalized at the tissue level and correlated with outcome. CONCLUSIONS: This immune-based combined multimodality signature, obtained through longitudinal peripheral blood monitoring and validated in an independent cohort, presents a novel means of predicting response early on during the treatment course. FUNDING: Daiichi Sankyo Inc, Cancer Research UK, EU IMI2 IMMUCAN, UK Medical Research Council, European Research Council (335326), Merck Serono. Cancer Research Institute, National Institute for Health Research, Guy's and St Thomas' NHS Foundation Trust and The Institute of Cancer Research. CLINICAL TRIAL NUMBER: NCT02633800