Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Unraveling the Mysterious Role of Palladium in Feigl bis(dimethylglyoximate)nickel(II) Spot Tests by Means of Confocal Raman Microscopy

The add protection effect promoted by traces of PdCl2 in [Ni(dmgH)(2)] spot tests was elucidated from confocal Raman microscopy imaging, which revealed the formation of protecting layers of [Pd(dmgH)(2)] closing the extremities of the [Ni(dmgH)(2)] filaments.

Looking over Toxin-K+ Channel Interactions. Clues from the Structural and Functional Characterization of alpha-KTx Toxin Tc32, a Kv1.3 Channel Blocker

alpha-KTx toxin Tc32, from the Amazonian scorpion Tityus cambridgei, lacks the dyad motif; including Lys27, characteristic of the family and generally associated with channel blockage. The toxin has been cloned and expressed for the first time. Electrophysiological experiments, by showing that the recombinant form blocks Kv1.3 channels of olfactory bulb periglomerular cells like the natural Tc32 toxin, when tested on the Kv1.3 channel of human T lymphocytes, confirmed it is in an active fold. The nuclear magnetic resonance-derived structure revealed it exhibits an alpha/beta scaffold typical of the members of the alpha-KTx family. TdK2 and TdK3, all belonging to the same alpha-KTx 18 subfamily, share significant sequence identity with Tc32 but diverse selectivity and affinity for Kv1.3 and Kv1.1 channels. To gain insight into the structural features that may justify those differences, we used the recombinant Tc32 nuclear magnetic resonance-derived structure to model the other two toxins, for which no experimental structure is available. Their interaction with Kv1.3 and Kv1.1 has been investigated by means of docking simulations. The results suggest that differences in the electrostatic features of the toxins and channels, in their contact surfaces, and in their total dipole moment orientations govern the affinity and selectivity of toxins. In addition, we found that, regardless of whether the dyad motif is present, it is always a Lys side chain that physically blocks the channels, irrespective of its position in the toxin sequence.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Endocrine symptom assessment in women with breast cancer: what a simple "yes" means

GOALS OF WORK: To investigate the self-reported symptoms related to endocrine therapy in women with early or advanced breast cancer and the impact of these symptoms on quality of life (QL) indicators. MATERIALS AND METHODS: Symptom occurrence was assessed by the Checklist for Patients on Endocrine Therapy (C-PET) and symptom intensity was assessed by linear analogue self-assessment (LASA) indicators. Patients also responded to global LASA indicators for physical well-being, mood, coping effort and treatment burden. Associations between symptoms and these indicators were analysed by linear regression models. MAIN RESULTS: Among 373 women, the distribution of symptom intensity showed considerable variation in patients reporting a symptom as present. Even though patients recorded a symptom as absent, some patients reported having experienced that symptom when responding to symptom intensity, as seen for decreased sex drive, tiredness and vaginal dryness. Six of 13 symptoms and lower age had a detrimental impact on the global indicators, particularly tiredness and irritability. CONCLUSIONS: Patients' experience of endocrine symptoms needs to be considered both in patient care and research, when interpreting the association between symptoms and QL.

Differentiating hippocampal subregions by means of quantitative magnetization transfer and relaxometry: preliminary results

The hippocampal formation (HF) of healthy control subjects and schizophrenic patients was examined using an MRI experiment that implements sequences for relaxometry and magnetization transfer (MT) quantification. In addition to the semi-quantitative magnetization transfer ratio (MTR), all of the observable properties of the binary spin bath model were included. The study demonstrates that, in contrast to the MTR, quantitative MT parameters (especially the T2 relaxation time of restricted protons, T2b) are capable to differentiate functionally significant subregions within the HF. The MT methodology appears to be a promising new tool for the differential microstructural evaluation of the HF in neuropsychiatric disorders accompanied by memory disturbances.

New means in spinal pedicle hook fixation. A biomechanical evaluation

Pedicle hooks which are used as an anchorage for posterior spinal instrumentation may be subjected to considerable three-dimensional forces. In order to achieve stronger attachment to the implantation site, hooks using screws for additional fixation have been developed. The failure loads and mechanisms of three such devices have been experimentally determined on human thoracic vertebrae: the Universal Spine System (USS) pedicle hook with one screw, a prototype pedicle hook with two screws and the Cotrel-Dubousset (CD) pedicle hook with screw. The USS hooks use 3.2-mm self-tapping fixation screws which pass into the pedicle, whereas the CD hook is stabilised with a 3-mm set screw pressing against the superior part of the facet joint. A clinically established 5-mm pedicle screw was tested for comparison. A matched pair experimental design was implemented to evaluate these implants in constrained (series I) and rotationally unconstrained (series II) posterior pull-out tests. In the constrained tests the pedicle screw was the strongest implant, with an average pull-out force of 1650 N (SD 623 N). The prototype hook was comparable, with an average failure load of 1530 N (SD 414 N). The average pull-out force of the USS hook with one screw was 910 N (SD 243 N), not significantly different to the CD hook's average failure load of 740 N (SD 189 N). The result of the unconstrained tests were similar, with the prototype hook being the strongest device (average 1617 N, SD 652 N). However, in this series the difference in failure load between the USS hook with one screw and the CD hook was significant. Average failure loads of 792 N (SD 184 N) for the USS hook and 464 N (SD 279 N) for the CD hook were measured. A pedicular fracture in the plane of the fixation screw was the most common failure mode for USS hooks.(ABSTRACT TRUNCATED AT 250 WORDS)

Phylogenetic analysis of Prevotella nigrescens, Prevotella intermedia and Porphyromonas gingivalis clinical strains reveals a clear species clustering

Prevotella nigrescens, Prevotella intermedia and Porphyromonas gingivalis are oral pathogens from the family Bacteroidaceae, regularly isolated from cases of gingivitis and periodontitis. In this study, the phylogenetic variability of these three bacterial species was investigated by means of 16S rRNA (rrs) gene sequence comparisons of a set of epidemiologically and geographically diverse isolates. For each of the three species, the rrs gene sequences of 11 clinical isolates as well as the corresponding type strains was determined. Comparison of all rrs sequences obtained with those of closely related species revealed a clear clustering of species, with only a little intraspecies variability but a clear difference in the rrs gene with respect to the next related taxon. The results indicate that the three species form stable, homogeneous genetic groups, which favours an rrs-based species identification of these oral pathogens. This is especially useful given the 7% sequence divergence between Prevotella intermedia and Prevotella nigrescens, since phenotypic distinction between the two Prevotella species is inconsistent or involves techniques not applicable in routine identification.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

Shaking head means "no"

A 45-year-old man was admitted to the emergency department because of twitching of the head. The patient took a tablet of sumatriptan every 3-4 h because of increasing head pain after a car accident. Owing to depression, the patient was on long-term treatment with venlafaxine. The patient presented as hypertensive, tachycardic, with dyskinesia and spontaneous myoclonic movements of the right sternocleidomastoid muscle. In a CT scan of the head and cervical spine any fractures, bleeding or damage of the vessels after the accident could be ruled out. After discontinuation of all serotonergic agents, administration of lorazepam symptoms resolved 24 h after the last intake of sumatriptan. Serotonin syndrome is a clinical diagnosis, which requires a high-index of diagnostic suspicion. Clinical features include a broad spectrum of symptoms ranging from mild to life-threatening manifestations. Management is based on removal of precipitating drugs and symptomatic care including benzodiazepines.

Extension of k-ratio methods to rank-based analyses

An extension of k-ratio multiple comparison methods to rank-based analyses is described. The new method is analogous to the Duncan-Godbold approximate k-ratio procedure for unequal sample sizes or correlated means. The close parallel of the new methods to the Duncan-Godbold approach is shown by demonstrating that they are based upon different parameterizations as starting points.^ A semi-parametric basis for the new methods is shown by starting from the Cox proportional hazards model, using Wald statistics. From there the log-rank and Gehan-Breslow-Wilcoxon methods may be seen as score statistic based methods.^ Simulations and analysis of a published data set are used to show the performance of the new methods. ^