Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.     

Mixture of KL subspaces for relevance feedback

Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user’s feedback to a single search session, performing a short-term learning. In this paper we present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term learning, the feedback history of all the users is exploited to create and update a representation of the images which is adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.

Relational Analysis and Precision via Probabilistic Abstract Interpretation

Within the context of a quantitative generalisation of the well established framework of Abstract Interpretation – i.e. Probabilistic Abstract Interpretation – we investigate a quantitative notion of precision which allows us to compare analyses on the basis of their expected exactness for a given program. We illustrate this approach by considering various types of numerical abstractions of the values of variables for independent analysis as well as weakly and fully relational analysis. We utilise for this a linear operator semantics of a simple imperative programming language. In this setting, fully relational dependencies are realised via the tensor product. Independent analyses and weakly relational analyses are realised as abstractions of the fully relational analysis.     

A simple method for improving local binary patterns by considering non-uniform patterns

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a high dimension, partially correlated, and introduce unwanted noise. To offset these disadvantages, we explore using random subspace, well-known to work well with noise and correlated features, to train features based also on non-uniform patterns. We find that a stand-alone support vector machine performs best with the uniform patterns and random subspace with histograms of 50 bins performs best with the non-uniform patterns. Superior results are obtained when the two are combined. Based on extensive experiments conducted in several domains using several benchmark databases, it is our conclusion that non-uniform patterns improve classifier performance.     

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients

DNA microarrays and RNA-based sequencing approaches are considered important discovery tools in clinical medicine. However, cross-platform reproducibility studies undertaken so far have highlighted that microarrays are not able to accurately measure gene expression, particularly when they are expressed at low levels. Here, we consider the employment of a digital PCR assay (ddPCR) to validate a gene signature previously identified by gene expression profile. This signature included ten Hedgehog (HH) pathways’ genes able to stratify multiple myeloma (MM) patients according to their self-renewal status. Results show that the designed assay is able to validate gene expression data, both in a retrospective as well as in a prospective cohort. In addition, the plasma cells’ differentiation status determined by ddPCR was further confirmed by other techniques, such as flow cytometry, allowing the identification of patients with immature plasma cells’ phenotype (i.e., expressing CD19+/CD81+ markers) upregulating HH genes, as compared to others, whose plasma cells lose the expression of these markers and were more differentiated. To our knowledge, this is the first technical report of gene expression data validation by ddPCR instead of classical qPCR. This approach permitted the identification of a Maturation Index through the integration of molecular and phenotypic data, able to possibly define upfront the differentiation status of MM patients that would be clinically relevant in the future.     

Chiral Discrimination Mechanisms by Silylated-Acetylated Cyclodextrins: Superficial Interactions vs. Inclusion

Cyclodextrin derivatives constitute a powerful class of auxiliary agents for the discrimination of apolar chiral substrates. Both host–guest inclusion phenomena and interactions with the derivatizing groups located on the surface of the macrocycle could drive the enantiodiscrimination; thus, it is important to understand the role that these processes play in the rational design of new chiral selectors. The purpose of this study is to compare via nuclear magnetic resonance (NMR) spectroscopy the efficiency of silylated-acetylated α-, β-, and γ-cyclodextrins in the chiral discrimination of 1,1,1,3,3-pentafluoro-2-(fluoromethoxy)-3-methoxypropane (compound B) and methyl 2-chloropropionate (MCP). NMR DOSY (Diffusion Ordered SpectroscopY) experiments were conducted for the determination of the bound molar fractions and the association constants, whereas ROESY (Rotating-frame Overhauser Enhancement SpectroscopY) measurements provided information on the hosts’ conformation and on the interaction phenomena with the guests. Compound B, endowed with fluorinated moieties, is not deeply included due to attractive Si-F interactions occurring at the external surface of the cyclodextrins. Therefore, a low selectivity toward the size of cyclodextrin cavity is found. By contrast, enantiodiscrimination of MCP relies on the optimal fitting between the size of the guest and that of the cyclodextrin cavity.     

Molecular Genetics of GLUT1DS Italian Pediatric Cohort: 10 Novel Disease-Related Variants and Structural Analysis

GLUT1 deficiency syndrome (GLUT1DS1; OMIM #606777) is a rare genetic metabolic disease, characterized by infantile-onset epileptic encephalopathy, global developmental delay, progressive microcephaly, and movement disorders (e.g., spasticity and dystonia). It is caused by heterozygous mutations in the SLC2A1 gene, which encodes the GLUT1 protein, a glucose transporter across the blood-brain barrier (BBB). Most commonly, these variants arise de novo resulting in sporadic cases, although several familial cases with AD inheritance pattern have been described. Twenty-seven Italian pediatric patients, clinically suspect of GLUT1DS from both sporadic and familial cases, have been enrolled. We detected by trios sequencing analysis 25 different variants causing GLUT1DS. Of these, 40% of the identified variants (10 out of 25) had never been reported before, including missense, frameshift, and splice site variants. Their structural mapping on the X-ray structure of GLUT1 strongly suggested the potential pathogenic effects of these novel disease-related mutations, broadening the genotypic spectrum heterogeneity found in the SLC2A1 gene. Moreover, 24% is located in a vulnerable region of the GLUT1 protein that involves transmembrane 4 and 5 helices encoded by exon 4, confirming a mutational hotspot in the SLC2A1 gene. Lastly, we investigated possible correlations between mutation type and clinical and biochemical data observed in our GLUT1DS cohort, revealing that splice site and frameshift variants are related to a more severe phenotype and low CSF parameters.     

Assembly of the Multi-Subunit Cytochrome bc1 Complex in the Yeast Saccharomyces cerevisiae

The cytochrome bc₁ complex is an essential component of the mitochondrial respiratory chain of the yeast Saccharomyces cerevisiae. It is composed of ten protein subunits, three of them playing an important role in electron transfer and proton pumping across the inner mitochondrial membrane. Cytochrome b, the central component of this respiratory complex, is encoded by the mitochondrial genome, whereas all the other subunits are of nuclear origin. The assembly of all these subunits into the mature and functional cytochrome bc₁ complex is therefore a complicated process which requires the participation of several chaperone proteins. It has been found that the assembly process of the mitochondrial bc₁ complex proceeds through the formation of distinct sub-complexes in an ordered sequence. Most of these sub-complexes have been thoroughly characterized, and their molecular compositions have also been defined. This study critically analyses the results obtained so far and highlights new possible areas of investigation.