NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy

Epilepsy surgery is an option for people with focal onset drug-resistant (DR) seizures but a delayed or incorrect diagnosis of epileptogenic zone (EZ) location limits its efficacy. Seizure semiological manifestations and their chronological appearance contain valuable information on the putative EZ location but their interpretation relies on extensive experience. The aim of our work is to support the localization of EZ in DR patients automatically analyzing the semiological description of seizures contained in video-EEG reports. Our sample is composed of 536 descriptions of seizures extracted from Electronic Medical Records of 122 patients. We devised numerical representations of anamnestic records and seizures descriptions, exploiting Natural Language Processing (NLP) techniques, and used them to feed Machine Learning (ML) models. We performed three binary classification tasks: localizing the EZ in the right or left hemisphere, temporal or extra-temporal, and frontal or posterior regions. Our computational pipeline reached performances above 70% in all tasks. These results show that NLP-based numerical representation combined with ML-based classification models may help in localizing the origin of the seizures relying only on seizures-related semiological text data alone. Accurate early recognition of EZ could enable a more appropriate patient management and a faster access to epilepsy surgery to potential candidates.

An overview of data integration in neuroscience with focus on Alzheimer's Disease

This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities. Finally, we provide an exemplary application of data integration to address Alzheimer's Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease's knowledge particularly in the perspective of an early AD diagnosis.

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings.

Where Do We Stand in Regularization for Life Science Studies?

More and more biologists and bioinformaticians turn to machine learning to analyze large amounts of data. In this context, it is crucial to understand which is the most suitable data analysis pipeline for achieving reliable results. This process may be challenging, due to a variety of factors, the most crucial ones being the data type and the general goal of the analysis (e.g., explorative or predictive). Life science data sets require further consideration as they often contain measures with a low signal-to-noise ratio, high-dimensional observations, and relatively few samples. In this complex setting, regularization, which can be defined as the introduction of additional information to solve an ill-posed problem, is the tool of choice to obtain robust models. Different regularization practices may be used depending both on characteristics of the data and of the question asked, and different choices may lead to different results. In this article, we provide a comprehensive description of the impact and importance of regularization techniques in life science studies. In particular, we provide an intuition of what regularization is and of the different ways it can be implemented and exploited. We propose four general life sciences problems in which regularization is fundamental and should be exploited for robustness. For each of these large families of problems, we enumerate different techniques as well as examples and case studies. Lastly, we provide a unified view of how to approach each data type with various regularization techniques.

Topologization of β-antimonene on Bi2Se3 via proximity effects

Topological surface states usually emerge at the boundary between a topological and a conventional insulator. Their precise physical character and spatial localization depend on the complex interplay between the chemical, structural and electronic properties of the two insulators in contact. Using a lattice-matched heterointerface of single and double bilayers of β-antimonene and bismuth selenide, we perform a comprehensive experimental and theoretical study of the chiral surface states by means of microscopy and spectroscopic measurements complemented by first-principles calculations. We demonstrate that, although β-antimonene is a trivial insulator in its free-standing form, it inherits the unique symmetry-protected spin texture from the substrate via a proximity effect that induces outward migration of the topological state. This "topologization" of β-antimonene is found to be driven by the hybridization of the bands from either side of the interface.

Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked

AIMS

The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes.

METHODS

The microbiome of 31 children with type 1 diabetes at onset and of 25 healthy children was determined using multiple polymorphic regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis to identify significant taxa and their metabolic pathways content.

RESULTS

Compared with healthy controls, patients showed a significantly higher relative abundance of the following most important taxa: Bacteroides stercoris, Bacteroides fragilis, Bacteroides intestinalis, Bifidobacterium bifidum, Gammaproteobacteria and its descendants, Holdemania, and Synergistetes and its descendants. On the contrary, the relative abundance of Bacteroides vulgatus, Deltaproteobacteria and its descendants, Parasutterella and the Lactobacillus, Turicibacter genera were significantly lower in patients with respect to healthy controls. The predicted metabolic pathway more associated with type 1 diabetes patients concerns "carbon metabolism," sugar and iron metabolisms in particular. Among the clinical variables considered, standardized body mass index, anti-insulin autoantibodies, glycemia, hemoglobin A1c, Tanner stage, and age at onset emerged as most significant positively or negatively correlated with specific clusters of taxa.

CONCLUSIONS

The relative abundance and supervised analyses confirmed the importance of B stercoris in type 1 diabetes patients at onset and showed a relevant role of Synergistetes and its descendants in patients with respect to healthy controls. In general the robustness and coherence of the showed results underline the relevance of studying the microbioma using multiple polymorphic regions, different types of analysis, and different approaches within each analysis.

A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease

Genome–wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer’s disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.

Secondary Somatic Mutations in G-Protein-Related Pathways and Mutation Signatures in Uveal Melanoma

BACKGROUND

Uveal melanoma (UM), a rare cancer of the eye, is characterized by initiating mutations in the genes G-protein subunit alpha Q (GNAQ), G-protein subunit alpha 11 (GNA11), cysteinyl leukotriene receptor 2 (CYSLTR2), and phospholipase C beta 4 (PLCB4) and by metastasis-promoting mutations in the genes splicing factor 3B1 (SF3B1), serine and arginine rich splicing factor 2 (SRSF2), and BRCA1-associated protein 1 (BAP1). Here, we tested the hypothesis that additional mutations, though occurring in only a few cases ("secondary drivers"), might influence tumor development.

METHODS

We analyzed all the 4125 mutations detected in exome sequencing datasets, comprising a total of 139 Ums, and tested the enrichment of secondary drivers in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that also contained the initiating mutations. We searched for additional mutations in the putative secondary driver gene protein tyrosine kinase 2 beta (PTK2B) and we developed new mutational signatures that explain the mutational pattern observed in UM.

RESULTS

Secondary drivers were significantly enriched in KEGG pathways that also contained GNAQ and GNA11, such as the calcium-signaling pathway. Many of the secondary drivers were known cancer driver genes and were strongly associated with metastasis and survival. We identified additional mutations in PTK2B. Sparse dictionary learning allowed for the identification of mutational signatures specific for UM.

CONCLUSIONS

A considerable part of rare mutations that occur in addition to known driver mutations are likely to affect tumor development and progression.

The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach

Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsing-remitting (RR) and secondary progressive (SP) form of multiple sclerosis (MS), to promptly identifying information useful to predict disease progression. For our analysis, a dataset of 3398 evaluations from 810 persons with MS (PwMS) was adopted. Three steps were provided: course classification; extraction of the most relevant predictors at the next time point; prediction if the patient will experience the transition from RR to SP at the next time point. The Current Course Assignment (CCA) step correctly assigned the current MS course with an accuracy of about 86.0%. The MS course at the next time point can be predicted using the predictors selected in CCA. PROs/CAOs Evolution Prediction (PEP) followed by Future Course Assignment (FCA) was able to foresee the course at the next time point with an accuracy of 82.6%. Our results suggest that PROs and CAOs could help the clinician decision-making in their practice.

Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network

INTRODUCTION

The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this paper we introduce Tangle, a time span-guided neural attention model that can accurately and timely predict the upcoming need for a second-line diabetes therapy from administrative data in the Australian adult population. The method is suitable for designing automatic therapy review recommendations for patients and their providers without the need to collect clinical measures.

DATA

We analyzed seven years of de-identified records (2008-2014) of the 10% publicly available linked sample of Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) electronic databases of Australia.

METHODS

By design, Tangle inherits the representational power of pre-trained word embedding, such as GloVe, to encode sequences of claims with the related MBS codes. Moreover, the proposed attention mechanism natively exploits the information hidden in the time span between two successive claims (measured in number of days). We compared the proposed method against state-of-the-art sequence classification methods.

RESULTS

Tangle outperforms state-of-the-art recurrent neural networks, including attention-based models. In particular, when the proposed time span-guided attention strategy is coupled with pre-trained embedding methods, the model performance reaches an Area Under the ROC Curve of 90%, an improvement of almost 10 percentage points over an attentionless recurrent architecture.

IMPLEMENTATION

Tangle is implemented in Python using Keras and it is hosted on GitHub at https://github.com/samuelefiorini/tangle.

Different Features of Tumor-Associated NK Cells in Patients With Low-Grade or High-Grade Peritoneal Carcinomatosis

Peritoneal carcinomatosis (PC) is a rare disease defined as diffused implantation of neoplastic cells in the peritoneal cavity. This clinical picture occurs during the evolution of peritoneal tumors, and it is the main cause of morbidity and mortality of patients affected by these pathologies, though cytoreductive surgery with heated intra-peritoneal chemotherapy (CRS/HIPEC) is yielding promising results. In the present study, we evaluated whether the tumor microenvironment of low-grade and high-grade PC could affect the phenotypic and functional features and thus the anti-tumor potential of NK cells. We show that while in the peritoneal fluid (PF) of low-grade PC most CD56dim NK cells show a relatively immature phenotype (NKG2A+KIR-CD57-CD16dim), in the PF of high-grade PC NK cells are, in large majority, mature (CD56dimKIR+CD57+CD16bright). Furthermore, in low-grade PC, PF-NK cells are characterized by a sharp down-regulation of some activating receptors, primarily NKp30 and DNAM-1, while, in high-grade PC, PF-NK cells display a higher expression of the PD-1 inhibitory checkpoint. The compromised phenotype observed in low-grade PC patients corresponds to a functional impairment. On the other hand, in the high-grade PC patients PF-NK cells show much more important defects that only partially reflect the compromised phenotype detected. These data suggest that the PC microenvironment may contribute to tumor escape from immune surveillance by inducing different NK cell impaired features leading to altered anti-tumor activity. Notably, after CRS/HIPEC treatment, the altered NK cell phenotype of a patient with a low-grade disease and favorable prognosis was reverted to a normal one. Our present data offer a clue for the development of new immunotherapeutic strategies capable of restoring the NK-mediated anti-tumor responses in association with the CRS/HIPEC treatment to increase the effectiveness of the current therapy.

Development of a smart post-hospitalization facility for older people by using domotics, robotics, and automated tele-monitoring

Recent studies showed that about the 8% of beds are occupied by patients who experience a delayed hospital discharge (DHD). This is attributed to a delay in the arrangement of home-care assistance or in admission to long-term care facilities. Recently a lot of technologies have been developed to improve caring and monitoring of older people. The aim of this study is to design, implement and test a prototype of a technology based post-hospitalization facility for older people at risk of DHD by using domotics, robotics and wearable sensors for tele-monitoring. A sensorised posthospitalization facility has been built inside the hospital. Thirty-five healthy volunteers aged from 20 to 82 years were recruited. Clinical and functional assessment, i.e. motility index (MI), and human-robot interaction satisfaction were measured. A significant correlation was observed between automatic MI and the Gait Speed, the time sit-to-stand, and the Timed Up and Go test. Domotics, robotics and technology-based telemonitoring may represent a new way to assess patient’s autonomy and functional and clinical conditions in an ecological way, reproducing as much as possible a real life at home.

New miRNA Signature Heralds Human NK Cell Subsets at Different Maturation Steps: Involvement of miR-146a-5p in the Regulation of KIR Expression

Natural killer cells are cytotoxic innate lymphoid cells that play an important role for early host defenses against infectious pathogens and surveillance against tumor. In humans, NK cells may be divided in various subsets on the basis of the relative CD56 expression and of the low-affinity FcγRIIIA CD16. In particular, the two main NK cell subsets are represented by the CD56^bright/CD16^−/dim and the CD56^dim/CD16^bright NK cells. Experimental evidences indicate that CD56^bright and CD56^dim NK cells represent different maturative stages of the NK cell developmental pathway. We identified multiple miRNAs differentially expressed in CD56^bright/CD16⁻ and CD56^dim/CD16^bright NK cells using both univariate and multivariate analyses. Among these, we found a few miRNAs with a consistent differential expression in the two NK cell subsets, and with an intermediate expression in the CD56^bright/CD16^dim NK cell subset, representing a transitional step of maturation of NK cells. These analyses allowed us to establish the existence of a miRNA signature able to efficiently discriminate the two main NK cell subsets regardless of their surface phenotype. In addition, by analyzing the putative targets of representative miRNAs we show that hsa-miR-146a-5p, may be involved in the regulation of killer Ig-like receptor (KIR) expression. These results contribute to a better understanding of the physiologic significance of miRNAs in the regulation of the development/function of human NK cells. Moreover, our results suggest that hsa-miR-146a-5p targeting, resulting in KIR down-regulation, may be exploited to generate/increment the effect of NK KIR-mismatching against HLA-class I⁺ tumor cells and thus improve the NK-mediated anti-tumor activity.

Data-driven strategies for robust forecast of continuous glucose monitoring time-series

Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task. Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.

A machine learning pipeline for multiple sclerosis course detection from clinical scales and patient reported outcomes

In this work we present a machine learning pipeline for the detection of multiple sclerosis course from a collection of inexpensive and non-invasive measures such as clinical scales and patient-reported outcomes. The proposed analysis is conducted on a dataset coming from a clinical study comprising 457 patients affected by multiple sclerosis. The 91 collected variables describe patients mobility, fatigue, cognitive performance, emotional status, bladder continence and quality of life. A preliminary data exploration phase suggests that the group of patients diagnosed as Relapsing-Remitting can be isolated from other clinical courses. Supervised learning algorithms are then applied to perform feature selection and course classification. Our results confirm that clinical scales and patient-reported outcomes can be used to classify Relapsing-Remitting patients.

Evidence of β-antimonene at the Sb/Bi2Se3 interface

We report a study of the interface between antimony and the prototypical topological insulator Bi2Se3. Scanning tunnelling microscopy measurements show the presence of ordered domains displaying a perfect lattice match with bismuth selenide. Density functional theory calculations of the most stable atomic configurations demonstrate that the ordered domains can be attributed to stacks of β-antimonene.

Proton Beam Radiotherapy of Uveal Melanoma: Italian Patients Treated in Nice, France

Purpose

To evaluate the results of 15 years of experience with proton beam radiotherapy in the treatment of intraocular melanoma, and to determine univariate and multivariate risk factors for local failure, eye retention, and survival.

Methods

A total of 368 cases of intraocular melanoma were treated with proton beam radiotherapy at Centre Lacassagne Cyclotron Biomedical of Nice, France, between 1991 and 2006. Actuarial methods were used to evaluate rate of local tumor control, eye retention, and survival after proton beam radiotherapy. Cox regression models were extracted to evaluate univariate risk factors, while regularized least squares algorithm was used to have a multivariate classification model to better discriminate risk factors.

Results

Tumor relapse occurred in 8.4% of the eyes, with a median recurrence time of 46 months. Enucleation was performed on 11.7% of the eyes after a median time of 49 months following proton beam; out of these, 29 eyes were enucleated due to relapse and 16 due to other causes. The univariate regression analysis identified tumor height and diameter as primary risk factors for enucleation. Regularized least squares analysis demonstrated the higher effectiveness of a multivariate model of five risk factors (macula distance, optic disc distance, tumor height, maximum diameter, and age) in discriminating relapsed vs nonrelapsed patients.

Conclusions

This data set, which is the largest in Italy with relatively long-term follow-up, demonstrates that a high rate of tumor control, survival, and eye retention were achieved after proton beam irradiation, as in other series.

Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge

Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functionally characterizing the obtained results. Recently Knowledge Driven Variable Selection (KDVS), an alternative approach which performs both steps at the same time, has been proposed. In this paper, we assess the effectiveness of KDVS against standard approaches on a Parkinson’s Disease (PD) dataset. The presented quantitative analysis is made possible by the construction of a reference list of genes and gene groups associated to PD. Our work shows that KDVS is much more effective than the standard approach in enhancing the interpretability of the obtained results.

Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm

Background

Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, the NB tumorigenesis is not still elucidated, although it is evident that genomic instability plays a critical role in the genesis of the tumor. Here we propose a mathematical approach to decipher genomic data and we provide a new model of NB metastatic tumorigenesis.

Method

We elucidate NB tumorigenesis using Enhanced Fused Lasso Latent Feature Model (E-FLLat) modeling the array comparative chromosome hybridization (aCGH) data of 190 metastatic NBs (63 stage 4S and 127 stage 4). This model for aCGH segmentation, based on the minimization of functional dictionary learning (DL), combines several penalties tailored to the specificities of aCGH data. In DL, the original signal is approximated by a linear weighted combination of atoms: the elements of the learned dictionary.

Results

The hierarchical structures for stage 4S shows at the first level of the oncogenetic tree several whole chromosome gains except to the unbalanced gains of 17q, 2p and 2q. Conversely, the high CNA complexity found in stage 4 tumors, requires two different trees. Both stage 4 oncogenetic trees are marked diverged, up to five sublevels and the 17q gain is the most common event at the first level (2/3 nodes). Moreover the 11q deletion, one of the major unfavorable marker of disease progression, occurs before 3p loss indicating that critical chromosome aberrations appear at early stages of tumorigenesis. Finally, we also observed a significant (p = 0.025) association between patient age and chromosome loss in stage 4 cases.

Conclusion

These results led us to propose a genome instability progressive model in which NB cells initiate with a DNA synthesis uncoupled from cell division, that leads to stage 4S tumors, primarily characterized by numerical aberrations, or stage 4 tumors with high levels of genome instability resulting in complex chromosome rearrangements associated with high tumor aggressiveness and rapid disease progression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0132-y) contains supplementary material, which is available to authorized users.

Dictionary learning improves subtyping of breast cancer aCGH data

The advent of Comparative Genomic Hybridization (CGH) data led to the development of new mathematical models and computational methods to automatically infer chromosomal alterations. In this work we tackle a standard clustering problem exploiting the good representation properties of a novel method based on dictionary learning. The identified dictionary atoms, which show co-occuring shared alterations among samples, can be easily interpreted by domain experts. We compare a state-of-the-art approach with an original method on a breast cancer dataset.

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

Pressure-induced inhomogeneous chiral-spin ground state in FeGe

(57)Fe nuclear forward scattering on the chiral magnet FeGe reveals an extremely large precursor phase region above the helimagnetic ordering temperature T(C)(p) and beyond the pressure-induced quantum phase transition at 19 GPa. The decrease of the magnetic hyperfine field ⟨B(hf)⟩ with pressure is accompanied by a large increase of the width of the distribution of ⟨B(hf)⟩, indicating a strong quasistatic inhomogeneity of the magnetic states in the precursor region. Hyperfine fields of the order of 4 T (equivalent to a magnetic moment μ(Fe)≈0.4μ(B)) persist up to 28.5 GPa. No signatures of magnetic order have been found at about 31 GPa. The results, supported by ab initio calculations, suggest that chiral magnetic precursor phenomena, such as an inhomogeneous chiral-spin state, are vastly enlarged due to increasing spin fluctuations as FeGe is tuned to its quantum phase transition.

Regenerative medicine for the treatment of Teno-desmic injuries of the equine. A series of 150 horses treated with platelet-derived growth factors

BACKGROUND/AIM

The aim of the present study was to evaluate the safety and the clinical outcome of platelet-rich plasma for the treatment of teno-desmic injures in competition horses.

PATIENTS AND METHODS

From January 2009 to December 2011, 150 sport horses suffering from teno-desmic injuries were treated with no-gelled platelet-concentrate.

RESULTS

No horse showed any major adverse reaction as a result of the procedure. Full healing was obtained for 81% of the horses. Twelve percent had clinical improvement and only 7% a failure. Eight percent of cases of relapse were observed. No statistically significant correlation existed between clinical outcome and the area of the lesion. A statistically significant correlation existed between the clinical outcome and the age of the horse.

CONCLUSION

Treatment with platelet-derived growth factors leads to the formation of a tendon with normal morphology and functionality, which translate in the resumption of the agonistic activity for the horses we treated.

Quantitative echography in primary uveal melanoma treated by proton beam therapy

OBJECTIVE

To describe the dynamics of thickness and internal reflectivity after proton beam therapy (PBT) in uveal melanoma.

PARTICIPANTS

One hundred and ninety-eight consecutive patients with choroidal or ciliary body melanoma treated by PBT were retrospectively considered.

METHODS

The post-PBT follow-up included ophthalmologic examination, retinography, and B and A modes of standardized echography every 6 months. A total of 1393 examinations were performed. We take into account 4 tumour categories according to the seventh TNM classification.

RESULTS

Before PBT, tumour thickness ranged from 1.5 to 12.5 mm with a mean of 5.9 mm. Its decrease after radiotherapy was best fitted by the sum of a first-order exponential decay and a constant with a decay half-life of 15 months. Based on the fit, tumour thickness stabilized on a constant value representing, on average, 47% of the initial value. Mean internal reflectivity before PBT was 68%. The dynamics of the reflectivity were best fitted by an exponential and a constant, with rise half-life of 11 months, and stability value of 87%.

CONCLUSIONS

We found that ultrasonographic dynamics of uveal melanoma treated by PBT resembles a function composed of the sum of a constant and a first-order exponential, as previously noted in studies on brachytherapy. Interestingly, after PBT, because of its shorter half-life, internal reflectivity has a faster dynamic response than thickness in large tumours, suggesting that increase of internal reflectivity is a more sensitive indicator of early response to therapy in larger tumours.

Two-dimensional electron gases at LaAlO3/SrTiO3 interfaces: orbital symmetry and hierarchy engineered by crystal orientation

Recent findings show the emergence of two-dimensional electron gases (2DEGs) at LaAlO(3)/SrTiO(3) interfaces along different orientations; yet details on band reconstructions have remained so far unknown. Via x-ray linear dichroism spectroscopy, we demonstrate that crystal symmetry imposes distinctive 2DEG orbital hierarchies on (001)-and (110)-oriented quantum wells, allowing selective occupancy of states of different symmetry. Such orientational tuning expands the possibilities for electronic engineering of 2DEGs and opens up enticing opportunities to understand the link between orbital symmetry and complex correlated states at LaAlO(3)/SrTiO(3) quantum wells.

Alternating proximal regularized dictionary learning

We present an algorithm for dictionary learning that is based on the alternating proximal algorithm studied by Attouch, Bolte, Redont, and Soubeyran (2010), coupled with a reliable and efficient dual algorithm for computation of the related proximity operators. This algorithm is suitable for a general dictionary learning model composed of a Bregman-type data fit term that accounts for the goodness of the representation and several convex penalization terms on the coefficients and atoms, explaining the prior knowledge at hand. As Attouch et al. recently proved, an alternating proximal scheme ensures better convergence properties than the simpler alternating minimization. We take care of the issue of inexactness in the computation of the involved proximity operators, giving a sound stopping criterion for the dual inner algorithm, which keeps under control the related errors, unavoidable for such a complex penalty terms, providing ultimately an overall effective procedure. Thanks to the generality of the proposed framework, we give an application in the context of genome-wide data understanding, revising the model proposed by Nowak, Hastie, Pollack, and Tibshirani (2011). The aim is to extract latent features (atoms) and perform segmentation on array-based comparative genomic hybridization (aCGH) data. We improve several important aspects that increase the quality and interpretability of the results. We show the effectiveness of the proposed model with two experiments on synthetic data, which highlight the enhancements over the original model.

The use of platelet-rich plasma gel in patients with mixed tumour undergoing superficial parotidectomy: a randomized study

BACKGROUND/AIM

Salivary gland tumors are mostly benign tumors. Whether a more conservative surgical approach at greater risk of recurrence, or a more radical intervention with an increased risk of facial paralysis is warranted is still under discussion. Our study addresses the opportunity for improving surgical outcome by employing platelet-rich plasma (PRP) gel at the surgical site.

PATIENTS AND METHODS

Twenty consecutive patients undergoing superficial parotidectomy were randomized and assigned to two groups, one with and one without PRP gel. Many parameters were evaluated after surgery and during follow-up, such as the duration of hospitalization, facial nerve deficit, onset of Frey's syndrome, relapse, cosmetic results, presence of keloid or scar depressions, behavior of several facial muscles.

RESULTS

Our explorative analysis suggests a positive effect of PRP on surgical outcome in patients undergoing parotidectomy, whereas no negative effects were detected.

CONCLUSION

This work suggests that administration of PRP in patients undergoing parotidectomy is beneficial.

Molecular fingerprinting reflects different histotypes and brain region in low grade gliomas

BACKGROUND

Paediatric low-grade gliomas (LGGs) encompass a heterogeneous set of tumours of different histologies, site of lesion, age and gender distribution, growth potential, morphological features, tendency to progression and clinical course. Among LGGs, Pilocytic astrocytomas (PAs) are the most common central nervous system (CNS) tumours in children. They are typically well-circumscribed, classified as grade I by the World Health Organization (WHO), but recurrence or progressive disease occurs in about 10-20% of cases. Despite radiological and neuropathological features deemed as classic are acknowledged, PA may present a bewildering variety of microscopic features. Indeed, tumours containing both neoplastic ganglion and astrocytic cells occur at a lower frequency.

METHODS

Gene expression profiling on 40 primary LGGs including PAs and mixed glial-neuronal tumours comprising gangliogliomas (GG) and desmoplastic infantile gangliogliomas (DIG) using Affymetrix array platform was performed. A biologically validated machine learning workflow for the identification of microarray-based gene signatures was devised. The method is based on a sparsity inducing regularization algorithm l₁l₂ that selects relevant variables and takes into account their correlation. The most significant genetic signatures emerging from gene-chip analysis were confirmed and validated by qPCR.

RESULTS

We identified an expression signature composed by a biologically validated list of 15 genes, able to distinguish infratentorial from supratentorial LGGs. In addition, a specific molecular fingerprinting distinguishes the supratentorial PAs from those originating in the posterior fossa. Lastly, within supratentorial tumours, we also identified a gene expression pattern composed by neurogenesis, cell motility and cell growth genes which dichotomize mixed glial-neuronal tumours versus PAs. Our results reinforce previous observations about aberrant activation of the mitogen-activated protein kinase (MAPK) pathway in LGGs, but still point to an active involvement of TGF-beta signaling pathway in the PA development and pick out some hitherto unreported genes worthy of further investigation for the mixed glial-neuronal tumours.

CONCLUSIONS

The identification of a brain region-specific gene signature suggests that LGGs, with similar pathological features but located at different sites, may be distinguishable on the basis of cancer genetics. Molecular fingerprinting seems to be able to better sub-classify such morphologically heterogeneous tumours and it is remarkable that mixed glial-neuronal tumours are strikingly separated from PAs.

Surface symmetry-breaking and strain effects on orbital occupancy in transition metal perovskite epitaxial films

The electron occupancy of 3d-orbitals determines the properties of transition metal oxides. This can be achieved, for example, through thin-film heterostructure engineering of ABO(3) oxides, enabling emerging properties at interfaces. Interestingly, epitaxial strain may break the degeneracy of 3d-e(g) and t(2g) orbitals, thus favoring a particular orbital filling with consequences for functional properties. Here we disclose the effects of symmetry breaking at free surfaces of ABO(3) perovskite epitaxial films and show that it can be combined with substrate-induced epitaxial strain to tailor at will the electron occupancy of in-plane and out-of-plane surface electronic orbitals. We use X-ray linear dichroism to monitor the relative contributions of surface, strain and atomic terminations to the occupancy of 3z(2)-r(2) and x(2)-y(2) orbitals in La(2/3)Sr(1/3)MnO(3) films. These findings open the possibility of an active tuning of surface electronic and magnetic properties as well as chemical properties (catalytic reactivity, wettability and so on).

Parameter space exploration within dynamic simulations of signaling networks

We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

Knowledge Driven Variable Selection (KDVS) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data

Background

High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power.

Results

We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method.

Conclusions

We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment–based approaches.

Revealing the insulating gap in α'-NaV (2)O(5) with resonant inelastic x-ray scattering

We measured the low energy excitation spectrum of α'-NaV (2)O(5) across its charge ordering and crystallographic phase transition with resonant inelastic x-ray scattering (RIXS) at the V L(3) edge. Exploiting the polarization dependence of the RIXS signal and the high resolution of the data, we reveal the excitation across the insulating gap at 1 eV and identify the excitations from occupied 3d(xy) bonding orbitals to unoccupied bonding 3d(xy) and 3d(yz)/3d(xz) orbitals. Furthermore we observe a progressive change of the electronic structure of α'-NaV (2)O(5) induced by soft x-ray irradiation, with the appearance of features characteristic of sodium deficient Na(x)V (2)O(5) (x < 1).

SVS: data and knowledge integration in computational biology

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.

Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment

MOTIVATION

The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods.

METHODS

We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state.

RESULTS

The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results.

A computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer's as a case study

BACKGROUND

A molecular characterization of Alzheimer's Disease (AD) is the key to the identification of altered gene sets that lead to AD progression. We rely on the assumption that candidate marker genes for a given disease belong to specific pathogenic pathways, and we aim at unveiling those pathways stable across tissues, treatments and measurement systems. In this context, we analyzed three heterogeneous datasets, two microarray gene expression sets and one protein abundance set, applying a recently proposed feature selection method based on regularization.

RESULTS

For each dataset we identified a signature that was successively evaluated both from the computational and functional characterization viewpoints, estimating the classification error and retrieving the most relevant biological knowledge from different repositories. Each signature includes genes already known to be related to AD and genes that are likely to be involved in the pathogenesis or in the disease progression. The integrated analysis revealed a meaningful overlap at the functional level.

CONCLUSIONS

The identification of three gene signatures showing a relevant overlap of pathways and ontologies, increases the likelihood of finding potential marker genes for AD.

A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients

BACKGROUND

Hypoxia is a condition of low oxygen tension occurring in the tumor microenvironment and it is related to poor prognosis in human cancer. To examine the relationship between hypoxia and neuroblastoma, we generated and tested an in vitro derived hypoxia gene signature for its ability to predict patients' outcome.

RESULTS

We obtained the gene expression profile of 11 hypoxic neuroblastoma cell lines and we derived a robust 62 probesets signature (NB-hypo) taking advantage of the strong discriminating power of the l1-l2 feature selection technique combined with the analysis of differential gene expression. We profiled gene expression of the tumors of 88 neuroblastoma patients and divided them according to the NB-hypo expression values by K-means clustering. The NB-hypo successfully stratifies the neuroblastoma patients into good and poor prognosis groups. Multivariate Cox analysis revealed that the NB-hypo is a significant independent predictor after controlling for commonly used risk factors including the amplification of MYCN oncogene. NB-hypo increases the resolution of the MYCN stratification by dividing patients with MYCN not amplified tumors in good and poor outcome suggesting that hypoxia is associated with the aggressiveness of neuroblastoma tumor independently from MYCN amplification.

CONCLUSIONS

Our results demonstrate that the NB-hypo is a novel and independent prognostic factor for neuroblastoma and support the view that hypoxia is negatively correlated with tumors' outcome. We show the power of the biology-driven approach in defining hypoxia as a critical molecular program in neuroblastoma and the potential for improvement in the current criteria for risk stratification.

Abstract 2002: Cell reprogramming by hypoxia

Background

Low oxygen tension (hypoxia) is an important determinant in tumor progression. In this study we assess the prognostic value of an in vitro derived hypoxia gene signature in neuroblastoma patients.

Material and Methods l1-l2 regularization framework has been applied on gene expression profiles of 11 neuroblastoma cell lines to define the neuroblastoma hypoxia signature. We applied k-means clustering on the expression level of the signature 62 probesets to segregate 88 neuroblastoma patients and subgroups obtained by common risk factors stratification. We analyzed the classes by Kaplan-Meier curves and log-rank test for overall survival (OS) and event-free survival (EFS). Multivariate Cox analysis was performed to define the predictive power of the signature.

Results

The neuroblastoma hypoxia signature distinguished two groups of neuroblastoma patients classifying them as poor prognosis (21 patients), those having OS rate of 25.5% and EFS rate of 27.7%, and as good prognosis (67 patients), those having OS rate of 73.2% and EFS rate of 67.7%. The poor prognosis patients show an over-expression of the hypoxia probesets. Multivariate Cox analysis revealed that the neuroblastoma hypoxia signature is a significant independent predictor after controlling for commonly used risk factors. When applied to MYCN not amplified patients, the hypoxia signature was capable to stratify patients with OS rate of 24.2% and EFS rate of 27.3% for the patients with poor prognosis, compared with OS rate of 81.4% and EFS rate of 74.8% for the patients with good prognosis.

Conclusions

We demonstrate that the NB-hypo signature is a significant prognostic factor capable of stratify neuroblastoma patients. Furthermore, we obtained the proof of principle that the approach of hypoxia genes selection from in vitro controlled tumor cell lines, is a feasible method to identify specific contribution of the microenvironment to the tumors’ biology.

Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2002.

Machine learning methods for predictive proteomics

The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.

Algebraic stability indicators for ranked lists in molecular profiling

MOTIVATION

We propose a method for studying the stability of biomarker lists obtained from functional genomics studies. It is common to adopt resampling methods to tune and evaluate marker-based diagnostic and prognostic systems in order to prevent selection bias. Such caution promotes honest estimation of class prediction, but leads to alternative sets of solutions. In microarray studies, the difference in lists may be bewildering, also due to the presence of modules of functionally related genes. Methods for assessing stability understand the dependency of the markers on the data or on the predictor's type and help selecting solutions.

RESULTS

A computational framework for comparing sets of ranked biomarker lists is presented. Notions and algorithms are based on concepts from permutation group theory. We introduce several algebraic indicators and metric methods for symmetric groups, including the Canberra distance, a weighted version of Spearman's footrule. We also consider distances between partial lists and an aggregation of sets of lists into an optimal list based on voting theory (Borda count). The stability indicators are applied in practical situations to several synthetic, cancer microarray and proteomics datasets. The addressed issues are predictive classification, presence of modules, comparison of alternative biomarker lists, outlier removal, control of selection bias by randomization techniques and enrichment analysis.

AVAILABILITY

Supplementary Material and software are available at the address http://biodcv.fbk.eu/listspy.html

A Grid Environment for High-Throughput Proteomics

We connect in a grid-enabled pipeline an ontology-based environment for proteomics spectra management with a machine learning platform for unbiased predictive analysis. We exploit two existing software platforms (MS-Analyzer and BioDCV), the emerging proteomics standards, and the middleware and computing resources of the EGEE Biomed VO grid infrastructure. In the setup, BioDCV is accessed by the MS-Analyzer workflow as a Web service, thus providing a complete grid environment for proteomics data analysis. Predictive classification studies on MALDI-TOF data based on this environment are presented.

High-pressure ground state of SmB6: electronic conduction and long range magnetic order

High-pressure 149Sm nuclear forward scattering of synchrotron radiation and specific heat measurements have been performed on the intermediate valent Kondo insulator SmB6. The results show that at a critical pressure p(c) approximately = 6 GPa, where the charge gap closes, a first order transition occurs to a magnetically ordered state, which shows typical features of trivalent samarium compounds. The similarity with SmS stresses the role of local correlations and gives important insight into the debate on the local or itinerant character of the f electrons in heavy fermion systems.

Building kernels from binary strings for image matching

In the statistical learning framework, the use of appropriate kernels may be the key for substantial improvement in solving a given problem. In essence, a kernel is a similarity measure between input points satisfying some mathematical requirements and possibly capturing the domain knowledge. In this paper, we focus on kernels for images: we represent the image information content with binary strings and discuss various bitwise manipulations obtained using logical operators and convolution with nonbinary stencils. In the theoretical contribution of our work, we show that histogram intersection is a Mercer's kernel and we determine the modifications under which a similarity measure based on the notion of Hausdorff distance is also a Mercer's kernel. In both cases, we determine explicitly the mapping from input to feature space. The presented experimental results support the relevance of our analysis for developing effective trainable systems.

Pressure-induced magnetic order in golden SmS

High pressure 149Sm nuclear forward scattering experiments have been performed on the nonmagnetic semiconductor SmS. We present the first clear evidence that the closure of the insulating gap at p(Delta) approximately 2 GPa coincides with the appearance of magnetic order. The pressure-induced magnetic phase transition has some first order character and suggests that the Sm ions are nearly trivalent at p(Delta). A Gamma(8) quartet crystal field ground state with a value of approximately 0.5 micro(B) for the samarium magnetic moment is inferred from our results. Considerable magnetic short range order is observed above the ordering temperature inferred from macroscopic measurements.