Miracle-dz/UniFAR-FTUs huggingface dataset:
Independent component analysis of normal and abnormal rhythm in twin pregnancies

We investigated the utility of ICA for evaluation of fetal rhythm in five uncomplicated twin pregnancies and in five twin pregnancies complicated by fetal arrhythmia. Using objective and subjective criteria, we sought to determine how the signal-to-noise ratio, signal fidelity and interference rejection are affected when synthesizing the fetal signal using all the signal-containing ICA components (rank-p ICA) versus using the single dominant component (rank-1 ICA). The signal of each fetus was most commonly distributed over 1 or 2 ICA components, as previously observed in studies of singleton pregnancies; however, in 8 of 26 (31%) cases the signal of each fetus was distributed over 3, 4 or even 5 ICA components. Rank-1 ICA provided the highest SNR and interference rejection, but at the cost of reduced signal fidelity. Our results corroborate that in twin pregnancies, including twin pregnancies complicated by fetal arrhythmia, rank-1 ICA is very effective in isolating the QRS complexes of each fetus; however, it has some limitations when used for fetal rhythm evaluation due to signal distortion. Occasionally, rank-1 ICA completely separates the P-wave and the T-wave from the QRS complex, thus requiring the mixing of several ICA components to achieve acceptable signal fidelity.
Suppression subtractive hybridization identified differentially expressed genes in colorectal cancer: microRNA-451a as a novel colorectal cancer–related gene

To investigate differentially expressed genes regulated by microRNA-451a in colorectal cancer. We detected expression of microRNA-451a in colorectal cancer samples and normal pericarcinous tissues from 68 colorectal cancer patients and the correlation between microRNA-451a and clinical features of these patients. Then, the expression of microRNA-451a in HCT116, SW620, HT29, SW480, and DLD cells was also measured. The suppression subtractive hybridization method was used with two HCT116 cell lines with overexpressing or underexpressing microRNA-451a, respectively. The most highly increased genes were screened. Their functions were predicted by gene ontology analysis. The expression ratio of microRNA-451a in colorectal cancer to pericarcinous tissues was 0.37. Expression of microRNA-451a was decreased in HCT116, SW620, HT29, SW480, and DLD cells. In our suppression subtractive hybridization library, expression of seven genes was the most highly increased when underexpressing microRNA-451a. They were BCAP31, EEF1A1, CDC20, WDR6, TUFM, RPL13, and RPL7A. Expression of DKK1, PSME1, NDUFA3, and GNB2 was most highly increased when overexpressing microRNA-451a. Gene ontology analysis showed that the main functions of these genes were associated with translational elongation, protein localization to the endoplasmic reticulum, translation, poly(A) RNA binding, negative regulations of Wnt signaling pathway, and so on. MicroRNA-451a was demonstrated to be downregulated in colorectal cancer patient tissues whose target genes were analyzed and functions were predicted by suppression subtractive hybridization.
Maillard Reaction Inhibitors Produced by Paecilomyces sp.

Maillard reaction inhibitors could be useful therapeutics for diabetes and other age-related diseases. We isolated for the first time 4-O-demethylsilvaticol (1) and (−)-mitorubrin (2) as Maillard reaction inhibitors from Paecilomyces sp. 3193B. Among the isolated inhibitors, 2 showed most potent inhibitory effect by an SDS–PAGE assay on cross-linked protein formation and by a fluorescent assay on AGE formation.
A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems.
Technical Brief: Mayo Clinic NLP System for Patient Smoking Status Identification

This article describes our system entry for the 2006 I2B2 contest "Challenges in Natural Language Processing for Clinical Data" for the task of identifying the smoking status of patients. Our system makes the simplifying assumption that patient-level smoking status determination can be achieved by accurately classifying individual sentences from a patient's record. We created our system with reusable text analysis components built on the Unstructured Information Management Architecture and Weka. This reuse of code minimized the development effort related specifically to our smoking status classifier. We report precision, recall, F-score, and 95% exact confidence intervals for each metric. Recasting the classification task for the sentence level and reusing code from other text analysis projects allowed us to quickly build a classification system that performs with a system F-score of 92.64 based on held-out data tests and of 85.57 on the formal evaluation data. Our general medical natural language engine is easily adaptable to a real-world medical informatics application. Some of the limitations as applied to the use-case are negation detection and temporal resolution.
Variable Bandwidth and Local Linear Regression

In this paper we introduce an appealing nonparametric method for estimating the mean regression function. The proposed method combines the ideas of local linear smoothers and variable bandwidth. Hence, it also inherits the advantages of both approaches. We give expressions for the conditional MSE and MISE of the estimator. Minimization of the MISE leads to an explicit formula for an optimal choice of the variable bandwidth. Moreover, the merits of considering a variable bandwidth are discussed. In addition, we show that the estimator does not have boundary effects, and hence does not require modifications at the boundary. The performance of a corresponding plug-in estimator is investigated. Simulations illustrate the proposed estimation method.
Kernel Structure Discovery for Gaussian Process Classification

This project was done at the Cambridge Machine Learning Group, as part of the larger effort to build an Automated Statistician. Given a data set, the Automated Statistician should run different methods to suggest interpretable hypotheses and the potential models to use for this data. In this project, we have shown that automatic kernel discovery can be achieved for GP classification. We implemented a kernel structure search procedure inspired by previous work on regression by other members of the Group. The evaluation on synthetic data sets demonstrated that the greedy procedure guided by approximate marginal likelihood can recover the underlying truth in these controlled experiments. The experiments on the four real-world biomedical data sets proved that our procedure achieves predictive performance on par with other recently proposed Bayesian models. Unlike the more complex models such as Additive GPs, our search procedure builds simple and interpretable kernels, similar to those that a human modeler might build when working on the given problem. We have shown that the constructed kernels reveal interesting and interpretable patterns in the data. To represent these patterns, we developed functionality for visualising the posterior means of the composite kernels’ components. These sets of plots correspond to the patterns identified in the data. This is the first step towards producing the automated reports for the end users, which we plan to pursue in further work.
Metabolic syndrome in childhood obesity.

OBJECTIVES We determined the frequency of metabolic risk factors and the prevalence of Metabolic Syndrome in childhood obesity. SUBJECTS 186 obese children (97 females and 89 males), aged 11.2 +/- 2.8 (6-16) years and 98 healthy children (46 females and 52 males), aged 10.9 +/- 3.2 (6-16) years were recruited for the study, as study and control groups, respectively. METHODS Subjects were evaluated for anthropometry, blood pressure (BP) and biochemical cardiovascular risk factors. Metabolic syndrome was defined in presence of > or = 3 of the following: (i) fasting triglyceride > or = 100 mg/dL; (ii) high density lipoprotein-cholesterol < 50 mg/dL, except in boys aged 15 to 19 years, in whom the cut-off point was 45 mg/dL; (iii) fasting glucose > or = 110 mg/dL; (iv) waist circumference > 75th percentile for age and gender and (v) systolic BP > 90th percentile. RESULTS We found that 144 (77.4%) children in the obese group had one, two or more cardiovascular risk factors. Using a pediatric definition, the prevalence of metabolic syndrome was 2.1%. In the control group, the clustering of one, two and three risk factors was very rare. CONCLUSION Childhood obesity is associated with increased frequency of cardiovascular risk factors and metabolic syndrome.
Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling

Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable workflow. State-of-art workflows suffer from the need for heuristic or subjective manual choices, high computational complexity, or strong parametric assumptions. We address these problems by establishing a theoretical link between multivariate/conditional independence testing, and model comparison in the multivariate predictive modelling aka supervised learning task. This link allows advances in the extensively studied supervised learning workflow to be directly transferred to independence testing workflows - including automated tuning of machine learning type which addresses the need for a heuristic choice, the ability to quantitatively trade-off computational demand with accuracy, and the modern black-box philosophy for checking and interfacing. As a practical implementation of this link between the two workflows, we present a python package 'pcit', which implements our novel multivariate and conditional independence tests, interfacing the supervised learning API of the scikit-learn package. Theory and package also allow for straightforward independence test based learning of graphical model structure. We empirically show that our proposed predictive independence test outperform or are on par to current practice, and the derived graphical model structure learning algorithms asymptotically recover the 'true' graph. This paper, and the 'pcit' package accompanying it, thus provide powerful, scalable, generalizable, and easy-to-use methods for multivariate and conditional independence testing, as well as for graphical model structure learning.
Direct Derivation of van der Waals Force Field Parameters from Quantum Mechanical Interaction Energies

van der Waals force field parameters are difficult to determine from only experimental data because of the insufficient data-to-parameter ratio, particularly for the diverse set of atom types necessary for force fields used in virtual ligand screening. We present a method which exploits the virtually unlimited number of ab initio calculations, as compared with experimental data, and does not have to rely on unphysical combination rules used by most force fields. Interaction energies of all pairs of molecules from a set of eight compounds at the MP2/d-aug-cc-pVDZ level with a counterpoise correction are used to simultaneously fit parameters in a buffered Lennard-Jones van der Waals potential for six atom types. The transferability of the parameters is demonstrated by the reproducibility of ab initio dimer energies for four molecules in a validation set. Next, sublimation energies of 14 alkanes and 11 nonalkane compounds are calculated using both our potential and the van der Waals potential from the MMFF94.
Combination Therapy and Noninvasive Imaging with a Dual Therapeutic Vector Expressing MDR1 Short Hairpin RNA and a Sodium Iodide Symporter

We investigated the feasibility of using combination gene therapy and noninvasive nuclear imaging after expression of the human sodium iodide symporter (hNIS) and inhibition of the multidrug resistance (MDR1) gene in colon cancer cells. Methods: HCT-15 cells were stably transfected with a dual expression vector, in which the hNIS gene, driven by a constitutive cytomegalovirus promoter, has been coupled to an MDR1 short hairpin RNA (shRNA) cassette. Cell lines stably expressing the hNIS gene and MDR1 shRNA (designated MN-61 and MN-62) were produced, and the expression of the NIS gene and MDR1 shRNA was examined by Western blotting, reverse transcription–polymerase chain reaction, and immunostaining. The functional activities of MDR1 shRNA were determined by paclitaxel uptake and sensitivity to doxorubicin. Functional NIS expression was confirmed by the uptake and efflux of 125I and the cytotoxicity of 131I. The effect of the combination of 131I and doxorubicin was determined by an in vitro clonogenic assay. In vivo NIS expression was examined by small-animal PET with 124I. Results: The shMDR-NIS–expressing cells showed a significant decrease in the expression of MDR1 messenger RNA and its translated product, P-glycoprotein. The inhibition of P-glycoprotein expression by shRNA enhanced the intracellular accumulation of paclitaxel, the cellular retention of which is mediated by P-glycoprotein, thereby increasing sensitivity to the anticancer drug. The shMDR-NIS–expressing cells showed a significant increase of 125I uptake, which was completely inhibited by KClO4. Although the iodide efflux rate was rapid, the cell survival rate was markedly reduced by 131I treatment. Interestingly, the combination of doxorubicin and a radioiodide (131I) displayed synergistic cytotoxicity that correlated with MDR1 inhibition and NIS expression in shMDR-NIS–expressing cells. Furthermore, in mice with shMDR-NIS–expressing tumor xenografts, small-animal PET with 124I clearly visualized shMDR1-NIS–expressing tumors. Conclusion: We developed a dual expression vector with the NIS gene and MDR1 shRNA. This study represents a promising first step in investigations of the potential use of a combination of the NIS gene and MDR1 shRNA as a new therapeutic strategy allowing RNA interference–based gene therapy, NIS-based radioiodine therapy, and in vivo monitoring based on NIS imaging.
Intelligent Rollups in Multidimensional OLAP Data

In this paper we propose a new operator for advanced exploration of large multidimensional databases. The proposed operator can automatically generalize from a specific problem case in detailed data and return the broadest context in which the problem occurs. Such a functionality would be useful to an analyst who after observing a problem case, say a drop in sales for a product in a store, would like to find the exact scope of the problem. With existing tools he would have to manually search around the problem tuple trying to draw a pattern. This process is both tedious and imprecise. Our proposed operator can automate these manual steps and return in a single step a compact and easy-to-interpret summary of all possible maximal generalizations along various roll-up paths around the case. We present a fle xible cost-based framework that can generalize various kinds of behaviour (not simply drops) while requiring little additional customization from the user. We design an algorithm that can work efficiently on large multidimensional hierarchical data cubes so as to be usable in an interactive setting.
Natural Hazards in a Changing World: A Case for Ecosystem-Based Management

Communities worldwide are increasingly affected by natural hazards such as floods, droughts, wildfires and storm-waves. However, the causes of these increases remain underexplored, often attributed to climate changes or changes in the patterns of human exposure. This paper aims to quantify the effect of climate change, as well as land cover change, on a suite of natural hazards. Changes to four natural hazards (floods, droughts, wildfires and storm-waves) were investigated through scenario-based models using land cover and climate change drivers as inputs. Findings showed that human-induced land cover changes are likely to increase natural hazards, in some cases quite substantially. Of the drivers explored, the uncontrolled spread of invasive alien trees was estimated to halve the monthly flows experienced during extremely dry periods, and also to double fire intensities. Changes to plantation forestry management shifted the 1∶100 year flood event to a 1∶80 year return period in the most extreme scenario. Severe 1∶100 year storm-waves were estimated to occur on an annual basis with only modest human-induced coastal hardening, predominantly from removal of coastal foredunes and infrastructure development. This study suggests that through appropriate land use management (e.g. clearing invasive alien trees, re-vegetating clear-felled forests, and restoring coastal foredunes), it would be possible to reduce the impacts of natural hazards to a large degree. It also highlights the value of intact and well-managed landscapes and their role in reducing the probabilities and impacts of extreme climate events.
Design and Partial Implementation of Health Care System for Disease Detection and Behavior Analysis by Using DM Techniques

Data Mining (DM) techniques such as classification, clustering, association, regression etc. are widely used in healthcare field in recent years to help improve the quality, efficiency as well as lowering the cost of developing healthcare systems. Especially, with the rapid development of the cloud platform services, which not only reduces the cost (time and expenditure), but also breaks the boundaries of data transactions among different systems and users. Therefore, it provides an effective way to reduce the time, and cost of software development as well as up-to-date services. In our work, we designed and partially implemented a healthcare system based on cloud services for disease detection and prediction using DM techniques in order to provide better services for both patients and health care givers. Although the system has been partially implemented, the experimental results are encouraging.
Family and schizophrenia - psychoeducational group in a pilot programme.

AIM The paper aims to present the programme, the outcomes and the conclusions of a psychoeducational training series for families with schizophrenia carried out by the Babiński Specialist Hospital in Kraków, Poland. The paper sets out to describe an explorative project based on qualitative study methods. The programme described in the paper adheres to the systemic and narrative approach to therapy. METHODS The project involved 23 people from 16 families, who attended 14 psychoeducational meetings for 4 months. A variety of methods was applied: lectures, case studies, discussion, interactive workshops, psycho-drawing, multimediapresentations, simulations of crisis situations, training in subgroups. RESULTS Upon the completion of the programme, no significant increase in the participants'theoretical knowledge on schizophrenia was observed, which in turn concurred with a decrease in the subjective sense of strain in the family. Even though only 11% of the participants initially expected emotional support from the group, 60% of them identified support as the most important benefit from the programme. CONCLUSIONS The systemic narrative approach used in the programme has been accepted and assessed well by families of patients with schizophrenia. It seems to provide an effective alternative to traditional psychotherapy. The combination of effective training tools with activities to create a space for participants to exchange their thoughts and experiences has proved to be an effective form of support to families in adversity.
Combining Ericksonian and Sandplay Approaches to Therapy with Children who Manifest Depression as a Developmental Barrier

This study reports on the use of the Ericksonian approach combined with sandplay therapy to assist children who present with depression, a troublesome developmental barrier. Participants were six children and/or adolescents with depression (two males and four females; age range 7 to 18; ethnicity: five white and one black). The intervention occurred once a week over a two-month period (eight 60-minute sessions in total). Outcome data were collected using qualitative interview and thematically analyzed. The Ericksonian approach to sandplay therapy had an overall positive effect on children who experienced depression as a developmental barrier.
Sloppiness and Emergent Theories in Physics, Biology, and Beyond

Large scale models of physical phenomena demand the development of new statistical and computational tools in order to be effective. Many such models are `sloppy', i.e., exhibit behavior controlled by a relatively small number of parameter combinations. We review an information theoretic framework for analyzing sloppy models. This formalism is based on the Fisher Information Matrix, which we interpret as a Riemannian metric on a parameterized space of models. Distance in this space is a measure of how distinguishable two models are based on their predictions. Sloppy model manifolds are bounded with a hierarchy of widths and extrinsic curvatures. We show how the manifold boundary approximation can extract the simple, hidden theory from complicated sloppy models. We attribute the success of simple effective models in physics as likewise emerging from complicated processes exhibiting a low effective dimensionality. We discuss the ramifications and consequences of sloppy models for biochemistry and science more generally. We suggest that the reason our complex world is understandable is due to the same fundamental reason: simple theories of macroscopic behavior are hidden inside complicated microscopic processes.
on Mouse Mastocytoma P815 Tumor-Specific Antigen Recognized by CTLs Identification of a Second Major

Murine mastocytoma P815 induces CTL responses against at least four distinct Ags (AB, C, D, and E). Recent studies have shown that the main component of the CTL response against the P815 tumor is targeted against Ags P815AB and P815E. The gene P1A has been well characterized. It encodes the P815AB Ag in the form of a nonameric peptide containing two epitopes, P815A and P815B, which are recognized by different CTLs. Here, we report the identiﬁcation of the P815E Ag. Using a cDNA library derived from tumor P815, we identiﬁed the gene coding for P815E. We also characterized the antigenic peptide that anti-P815E CTLs recognize on the MHC class I molecule H-2K d . The P815E Ag results from a mutation within an ubiquitously expressed gene encoding methionine sulfoxide reductase, an enzyme that is believed to be important in the protection of proteins against the by-products of aerobic metabolism. Surprisingly, immunizing mice i.p. with syngeneic tumor cells (L1210) that were constructed to express B7-1 and P815E did not induce resistance against live P815, even though a strong anti-P815E CTL response was observed with splenocytes from immunized animals. The Journal of Immunology, 1999, 162: 3534–3540.