A cluster analytic approach to identifying predictors and moderators of psychosocial treatment for bipolar depression: results from STEP-BD

Background We sought to address how predictors and moderators of psychotherapy for bipolar depression - identified individually in prior analyses - can inform the development of a metric for prospectively classifying treatment outcome in intensive psychotherapy (IP) versus collaborative care (CC) adjunctive to pharmacotherapy in the Systematic Treatment Enhancement Program (STEP-BD) study. Methods We conducted post-hoc analyses on 135 STEP-BD participants using cluster analysis to identify subsets of participants with similar clinical profiles and investigated this combined metric as a moderator and predictor of response to IP. We used agglomerative hierarchical cluster analyses and k-means clustering to determine the content of the clinical profiles. Logistic regression and Cox proportional hazard models were used to evaluate whether the resulting clusters predicted or moderated likelihood of recovery or time until recovery. Results The cluster analysis yielded a two-cluster solution: 1) "less-recurrent/severe" and 2) "chronic/recurrent." Rates of recovery in IP were similar for less-recurrent/severe and chronic/recurrent participants. Less-recurrent/severe patients were more likely than chronic/recurrent patients to achieve recovery in CC (p=.040, OR=4.56). IP yielded a faster recovery for chronic/recurrent participants, whereas CC led to recovery sooner in the less-recurrent/severe cluster (p=.034, OR=2.62). Limitations Cluster analyses require list-wise deletion of cases with missing data so we were unable to conduct analyses on all STEP-BD participants. Conclusions A well-powered, parametric approach can distinguish patients based on illness history and provide clinicians with symptom profiles of patients that confer differential prognosis in CC vs. IP.

Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes

Hierarchical Dirichlet processes (HDP) was originally designed and experimented for a single data channel. In this paper we enhanced its ability to model heterogeneous data using a richer structure for the base measure being a product-space. The enhanced model, called Product Space HDP (PS-HDP), can (1) simultaneously model heterogeneous data from multiple sources in a Bayesian nonparametric framework and (2) discover multilevel latent structures from data to result in different types of topics/latent structures that can be explained jointly. We experimented with the MDC dataset, a large and real-world data collected from mobile phones. Our goal was to discover identity–location– time (a.k.a who-where-when) patterns at different levels (globally for all groups and locally for each group). We provided analysis on the activities and patterns learned from our model, visualized, compared and contrasted with the ground-truth to demonstrate the merit of the proposed framework. We further quantitatively evaluated and reported its performance using standard metrics including F1-score, NMI, RI, and purity. We also compared the performance of the PS-HDP model with those of popular existing clustering methods (including K-Means, NNMF, GMM, DP-Means, and AP). Lastly, we demonstrate the ability of the model in learning activities with missing data, a common problem encountered in pervasive and ubiquitous computing applications.

A hybrid approach to clustering in big data

Clustering of big data has received much attention recently. In this paper, we present a new clusiVAT algorithm and compare it with four other popular data clustering algorithms. Three of the four comparison methods are based on the well known, classical batch k-means model. Specifically, we use k-means, single pass k-means, online k-means, and clustering using representatives (CURE) for numerical comparisons. clusiVAT is based on sampling the data, imaging the reordered distance matrix to estimate the number of clusters in the data visually, clustering the samples using a relative of single linkage (SL), and then noniteratively extending the labels to the rest of the data-set using the nearest prototype rule. Previous work has established that clusiVAT produces true SL clusters in compact-separated data. We have performed experiments to show that k-means and its modified algorithms suffer from initialization issues that cause many failures. On the other hand, clusiVAT needs no initialization, and almost always finds partitions that accurately match ground truth labels in labeled data. CURE also finds SL type partitions but is much slower than the other four algorithms. In our experiments, clusiVAT proves to be the fastest and most accurate of the five algorithms; e.g., it recovers 97% of the ground truth labels in the real world KDD-99 cup data (4 292 637 samples in 41 dimensions) in 76 s.

Immobilization of large, aliovalent cations in the small-pore zeolite K-natrolite by means of pressure

High-pressure ion exchange of small-pore zeolite K-natrolite allows immobilization of nominally non-exchangeable aliovalent cations such as trivalent europium. A sample exchanged at 3.0(1) GPa and 250 °C contains about 4.7 Eu^III ions per unit cell, which is equivalent to over 90 % of the K⁺ cations being exchanged.

Some results on Lipschitz quasi-arithmetic means

We present in this paper some properties of k-Lipschitz quasi-arithmetic means. The Lipschitz aggregation operations are stable with respect to input inaccuracies, what is a very important property for applications. Moreover, we provide sufficient conditions to determine when a quasi–arithemetic mean holds the k-Lipschitz property and allow us to calculate the Lipschitz constant k.

New Compounds Bearing [M(S2O7)3]2– Anions (M = Si, Ge, Sn): Syntheses and Characterization of A2[Si(S2O7)3] (A = Na, K, Rb), A2[Ge(S2O7)3] (A = Li, Na, K, Rb, Cs), A2[Sn(S2O7)3] (A = Na, K), and the Unique Germanate Hg2[Ge(S2O7)3]Cl2 with Cationic 1∞[

The reaction of the group 14 tetrachlorides MCl4 (M = Si, Ge, Sn) with oleum (65 % SO3) at elevated temperatures led to the unique anionic complexes [M(S2O7)3]2– that show the central M atoms in coordination of three chelating S2O72– groups. The mean distances M–O within the complexes increase from 175 pm (M = Si) via 186 pm (M = Ge) up to 200 pm (M = Sn). The charge balance for the [M(S2O7)3]2– anions is achieved by alkaline metal ions A+ (A = Li, Na, K, Rb, Cs) which were implemented in the syntheses in form of their sulfates. The size of the A+ ions, i.e. their coordination requirement causes the crystallographic differences in the crystal structures, while the structure of the complex [M(S2O7)3]2– anions remains essentially unaffected. Furthermore, we were able to characterize the unique germanate Hg2[Ge(S2O7)3]Cl2 which forms when HgCl2 is added as a source for the counter cation. The Hg2+ and the Cl– ions form infinite cationic chains according to 1∞[HgCl2/2]+ which take care for the charge compensation. For selected examples of the compounds the thermal behavior has been monitored by means of thermal analyses and X-ray powder diffraction. For A being an alkaline metal the decomposition product is a mixture of the sulfates A2SO4 and the dioxides MO2, whereas Hg2[Ge(S2O7)3]Cl2 shows a more complicated decomposition. The tris-(disulfato)-silicate Na2[Si(S2O7)3] has additionally been examined by solid state 29Si and 23Na NMR spectroscopic measurements.

Adaptive-multi-reference least means squares filter

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.