Finite-dimensional representations of the quantum superalgebra Uq[gl(n/m)]U_q[gl(n/m)] and related q-identities

Explicit expressions for the generators of the quantum superalgebra $U_q[gl(n/m)]$ acting on a class of irreducible representations are given. The class under consideration consists of all essentially typical representations: for these a Gel'fand-Zetlin basis is known. The verification of the quantum superalgebra relations to be satisfied is shown to reduce to a set of $q$-number identities.Comment: 12 page

Hopf algebras and Markov chains: Two examples and a theory

The operation of squaring (coproduct followed by product) in a combinatorial Hopf algebra is shown to induce a Markov chain in natural bases. Chains constructed in this way include widely studied methods of card shuffling, a natural "rock-breaking" process, and Markov chains on simplicial complexes. Many of these chains can be explictly diagonalized using the primitive elements of the algebra and the combinatorics of the free Lie algebra. For card shuffling, this gives an explicit description of the eigenvectors. For rock-breaking, an explicit description of the quasi-stationary distribution and sharp rates to absorption follow.Comment: 51 pages, 17 figures. (Typographical errors corrected. Further fixes will only appear on the version on Amy Pang's website, the arXiv version will not be updated.

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Optimisation of design and operation of MSF desalination process using MINLP technique in gPROMS

NoOptimal design and operation of MSF desalination process is considered here using MINLP technique within gPROMS model builder 2.3.4. gPROMS provides an easy and flexible platform to build a process flowsheet graphically and the corresponding master model connecting automatically individual unit model equations during simulation and optimisation. For different freshwater demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in of process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents

Real time testing of CN and M8S earthquake prediction algorithms in Italy

EGU2007 General Assembly, Vienna, Austria (Aprile 15-20, 2007)

Structural neural markers of response to cognitive behavioral therapy in pediatric obsessive-compulsive disorder

Background Cognitive behavioral therapy (CBT) is an effective, first-line treatment for pediatric obsessive-compulsive disorder (OCD). While neural predictors of treatment outcomes have been identified in adults with OCD, robust predictors are lacking for pediatric patients. Herein, we sought to identify brain structural markers of CBT response in youth with OCD. Methods Twenty-eight children/adolescents with OCD and 27 matched healthy participants (7- to 18-year-olds, M = 11.71 years, SD = 3.29) completed high-resolution structural and diffusion MRI (all unmedicated at time of scanning). Patients with OCD then completed 12-16 sessions of CBT. Subcortical volume and cortical thickness were estimated using FreeSurfer. Structural connectivity (streamline counts) was estimated using MRtrix. Results Thinner cortex in nine frontoparietal regions significantly predicted improvement in Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) scores (all ts > 3.4, FDR-corrected ps < .05). These included middle and superior frontal, angular, lingual, precentral, superior temporal, and supramarginal gyri (SMG). Vertex-wise analyses confirmed a significant left SMG cluster, showing large effect size (Cohen's d = 1.42) with 72.22% specificity and 90.00% sensitivity in predicting CBT response. Ten structural connections between cingulo-opercular regions exhibited fewer streamline counts in OCD (all ts > 3.12, Cohen's ds > 0.92) compared with healthy participants. These connections predicted post-treatment CY-BOCS scores, beyond pretreatment severity and demographics, though not above and beyond cortical thickness. Conclusions The current study identified group differences in structural connectivity (reduced among cingulo-opercular regions) and cortical thickness predictors of CBT response (thinner frontoparietal cortices) in unmedicated children/adolescents with OCD. These data suggest, for the first time, that cortical and white matter features of task control circuits may be useful in identifying which pediatric patients respond best to individual CBT.Y

Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data

Background: Internet cognitive behavioural therapy (iCBT) is a viable delivery format of CBT for depression. However, iCBT programmes include training in a wide array of cognitive and behavioural skills via different delivery methods, and it remains unclear which of these components are more efficacious and for whom. Methods: We did a systematic review and individual participant data component network meta-analysis (cNMA) of iCBT trials for depression. We searched PubMed, PsycINFO, Embase, and the Cochrane Library for randomised controlled trials (RCTs) published from database inception to Jan 1, 2019, that compared any form of iCBT against another or a control condition in the acute treatment of adults (aged ≥18 years) with depression. Studies with inpatients or patients with bipolar depression were excluded. We sought individual participant data from the original authors. When these data were unavailable, we used aggregate data. Two independent researchers identified the included components. The primary outcome was depression severity, expressed as incremental mean difference (iMD) in the Patient Health Questionnaire-9 (PHQ-9) scores when a component is added to a treatment. We developed a web app that estimates relative efficacies between any two combinations of components, given baseline patient characteristics. This study is registered in PROSPERO, CRD42018104683. Findings: We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42·0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD −1·83 [95% credible interval (CrI) −2·90 to −0·80]) and that relaxation might be harmful (1·20 [95% CrI 0·17 to 2·27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0·32 [95% CrI 0·13 to 0·93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components. Interpretation: The individual patient data cNMA revealed potentially helpful, less helpful, or harmful components and delivery formats for iCBT packages. iCBT packages aiming to be effective and efficient might choose to include beneficial components and exclude ones that are potentially detrimental. Our web app can facilitate shared decision making by therapist and patient in choosing their preferred iCBT package. Funding: Japan Society for the Promotion of Science