A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.Comment: Genetic and Evolutionary Computation Conference (GECCO) 2017, Berlin, German

Case management

Provides an overview of methods of diagnosis, treatment and patient care in complex emergencies. This chapter: ■ provides guidelines for initial management, including emergency triage – for the rapid identifi cation and treatment of patients at greatest risk of dying – and clinical assessment ■ discusses confi rmatory diagnosis of malaria using microscopy and rapid diagnostic tests (RDTs) ■ outlines factors that determine the choice of antimalarial drug ■ describes antimalarial drug treatment for uncomplicated malaria, subsequent follow-up, and the management of treatment failures ■ describes the assessment and treatment of anaemia ■ describes the treatment of severe P. falciparum malaria and its associated complications, including resuscitation, treatment with antimalarial drugs, and nursing care ■ provides guidance on the prevention and treatment of malaria in special groups (pregnant women, malnourished patients, returning refugees and displaced persons)

A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer.

The recognition that colorectal cancer (CRC) is a heterogeneous disease in terms of clinical behaviour and response to therapy translates into an urgent need for robust molecular disease subclassifiers that can explain this heterogeneity beyond current parameters (MSI, KRAS, BRAF). Attempts to fill this gap are emerging. The Cancer Genome Atlas (TGCA) reported two main CRC groups, based on the incidence and spectrum of mutated genes, and another paper reported an EMT expression signature defined subgroup. We performed a prior free analysis of CRC heterogeneity on 1113 CRC gene expression profiles and confronted our findings to established molecular determinants and clinical, histopathological and survival data. Unsupervised clustering based on gene modules allowed us to distinguish at least five different gene expression CRC subtypes, which we call surface crypt-like, lower crypt-like, CIMP-H-like, mesenchymal and mixed. A gene set enrichment analysis combined with literature search of gene module members identified distinct biological motifs in different subtypes. The subtypes, which were not derived based on outcome, nonetheless showed differences in prognosis. Known gene copy number variations and mutations in key cancer-associated genes differed between subtypes, but the subtypes provided molecular information beyond that contained in these variables. Morphological features significantly differed between subtypes. The objective existence of the subtypes and their clinical and molecular characteristics were validated in an independent set of 720 CRC expression profiles. Our subtypes provide a novel perspective on the heterogeneity of CRC. The proposed subtypes should be further explored retrospectively on existing clinical trial datasets and, when sufficiently robust, be prospectively assessed for clinical relevance in terms of prognosis and treatment response predictive capacity. Original microarray data were uploaded to the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress/) under Accession Nos E-MTAB-990 and E-MTAB-1026. © 2013 Swiss Institute of Bioinformatics. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland

An Integrated Multiscale Method for the Characterisation of Active Faults in Offshore Areas. The Case of Sant\u2019Eufemia Gulf (Offshore Calabria, Italy)

Diagnostic morphological features (e.g., rectilinear seafloor scarps) and lateral offsets of the Upper Quaternary deposits are used to infer active faults in offshore areas. Although they deform a significant seafloor region, the active faults are not necessarily capable of producing large earthquakes as they correspond to shallow structures formed in response to local stresses. We present a multiscale approach to reconstruct the structural pattern in offshore areas and distinguish between shallow, non-seismogenic, active faults, and deep blind faults, potentially associated with large seismic moment release. The approach is based on the interpretation of marine seismic reflection data and quantitative morphometric analysis of multibeam bathymetry, and tested on the Sant\u2019Eufemia Gulf (southeastern Tyrrhenian Sea). Data highlights the occurrence of three major tectonic events since the Late Miocene. The first extensional or transtensional phase occurred during the Late Miocene. Since the Early Pliocene, a right-lateral transpressional tectonic event caused the positive inversion of deep (>3 km) tectonic features, and the formation of NE-SW faults in the central sector of the gulf. Also, NNE-SSW to NE-SW trending anticlines (e.g., Maida Ridge) developed in the eastern part of the area. Since the Early Pleistocene (Calabrian), shallow (<1.5 km) NNE-SSW oriented structures formed in a left-lateral transtensional regime. The new results integrated with previous literature indicates that the Late Miocene to Recent transpressional/transtensional structures developed in an 3cE-W oriented main displacement zone that extends from the Sant\u2019Eufemia Gulf to the Squillace Basin (Ionian offshore), and likely represents the upper plate response to a tear fault of the lower plate. The quantitative morphometric analysis of the study area and the bathymetric analysis of the Angitola Canyon indicate that NNE-SSW to NE-SW trending anticlines were negatively reactivated during the last tectonic phase. We also suggest that the deep structure below the Maida Ridge may correspond to the seismogenic source of the large magnitude earthquake that struck the western Calabrian region in 1905. The multiscale approach contributes to understanding the tectonic imprint of active faults from different hierarchical orders and the geometry of seismogenic faults developed in a lithospheric strike-slip zone orthogonal to the Calabrian Arc