Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Discovery and System Biology in molecular medicine: an application to neurodegenerative diseases

The possibility to study an organism in terms of system theory has been proposed in the past, but only the advancement of molecular biology techniques allow us to investigate the dynamical properties of a biological system in a more quantitative and rational way than before . These new techniques can gave only the basic level view of an organisms functionality. The comprehension of its dynamical behaviour depends on the possibility to perform a multiple level analysis. Functional genomics has stimulated the interest in the investigation the dynamical behaviour of an organism as a whole. These activities are commonly known as System Biology, and its interests ranges from molecules to organs. One of the more promising applications is the 'disease modeling'. The use of experimental models is a common procedure in pharmacological and clinical researches; today this approach is supported by 'in silico' predictive methods. This investigation can be improved by a combination of experimental and computational tools. The Machine Learning (ML) tools are able to process different heterogeneous data sources, taking into account this peculiarity, they could be fruitfully applied to support a multilevel data processing (molecular, cellular and morphological) that is the prerequisite for the formal model design; these techniques can allow us to extract the knowledge for mathematical model development. The aim of our work is the development and implementation of a system that combines ML and dynamical models simulations. The program is addressed to the virtual analysis of the pathways involved in neurodegenerative diseases. These pathologies are multifactorial diseases and the relevance of the different factors has not yet been well elucidated. This is a very complex task; in order to test the integrative approach our program has been limited to the analysis of the effects of a specific protein, the Cyclin dependent kinase 5 (CDK5) which relies on the induction of neuronal apoptosis. The system has a modular structure centred on a textual knowledge discovery approach. The text mining is the only way to enhance the capability to extract ,from multiple data sources, the information required for the dynamical simulator. The user may access the publically available modules through the following site: http://biocomp.ge. ismac.cnr. it.
Alteration of nuclear lamin organization inhibits RNA polymerase II–dependent transcription

RTegulation of gene activity is mediated by alterations in chromatin organization. In addition, chromatin organization may be governed in part by interactions with structural components of the nucleus. The nuclear lamins comprise the lamina and a variety of nucleoplasmic assemblies that together are major structural components of the nucleus. Furthermore, lamins and lamin-associated proteins have been reported to bind chromatin. These observations suggest that the nuclear lamins may be involved in the regulation of gene activity. In this report, we test this possibility by disrupting the normal organization of nuclear lamins with a dominant negative lamin mutant lacking the NH2-terminal domain. We find that this disruption inhibits RNA polymerase II activity in both mammalian cells and transcriptionally active embryonic nuclei from Xenopus laevis. The inhibition appears to be specific for polymerase II as disruption of lamin organization does not detectably inhibit RNA polymerases I and III. Furthermore, immunofluorescence observations indicate that this selective inhibition of polymerase II–dependent transcription involves the TATA binding protein, a component of the basal transcription factor TFIID.
Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD

Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins (N = 627 (63% females) including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725 (58% females) including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention (r = 0.17, p-value = 7.4E-5) and Hyperactivity-Impulsivity (r = 0.16, p-value = 2.9E-4), and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of rg = 0.24 [0.02, 0.43] and rg = 0.23 [0.07, 0.39]. Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity.
Development and validation of a dietary screening tool for high sodium consumption in Australian renal patients.

OBJECTIVE The study objective was to develop and evaluate the feasibility and validity of a self-administered Scored Sodium Questionnaire (SSQ) for use in the routine clinical care of Australian chronic kidney disease (CKD) patients. DESIGN AND METHODS The study took place in community-based outreach clinics using a multidisciplinary model of care. Assessment of sources of dietary sodium intake in the target population used comprehensive diet history interviews (Phase 1) to inform development of a 10-item food frequency questionnaire that was scored and validated using 24-hour urinary sodium and 2 alternative dietary intake methods (Phase 2). Subjects were adults with CKD Stages 3 to 5 (Phase 1 n = 30; Phase 2 n = 47). INTERVENTION On a single day, participants (n = 47) completed the SSQ, feasibility survey, 24-hour urine collection, and 24-hour food record. A diet history interview was also conducted to confirm sodium intake on the day of data collection reflected habitual intake. MAIN OUTCOME MEASURE Validity of the SSQ score was confirmed by correlation with 24-hour urine sodium. Validity of a cutpoint on the SSQ score to correctly identify high- versus low-sodium consumers was confirmed by receiver operating characteristic curve analysis: area under the curve, sensitivity, and specificity. RESULTS Total SSQ score correlated significantly with 24-hour urine sodium (r = 0.371; P = .031). Correlation between 24-hour food record and diet history sodium confirmed consumption on the data collection day reflected habitual intake (r = 0.701; P ≤ .001). A cutpoint of 65 or greater on the SSQ score was confirmed as valid to identify high-sodium consumers: area under the curve 0.713, sensitivity 61%, and specificity 82%. CONCLUSION The SSQ is feasible and valid to assess habitual sodium intake in the Australian CKD population and to identify high-sodium consumers for referral to individualized counseling on a low-sodium diet.
Universality and scaling in networks of period-doubling maps with a pacemaker

The networks of globally coupled maps with a pacemaker have been introduced. We consider a generalization of the Kaneko model with a pacemaker represented by a single period-doubling element coupled unidirectionally with a set of other mutually coupled cells. We also investigate the dynamics of a system of two unidirectionally coupled elements, which manifests a special type of critical behaviour, known as bicriticality, at the point of simultaneous transition to chaos in both subsystems. With the help of the renormalization group (RG), we show for a case of two mutually coupled bicritical maps with a pacemaker that there are two types of coupling: dissipative and inertial. We investigate the dynamics of a network with a pacemaker with two types of global coupling and the properties of universality and scaling in this system.
ConTexter feedback system

Today's large-scale softwareintensive systems exhibit an increasing complexity due to a broad spectrum of technical and socio-technical components. Due to the very dynamic character of such systems as well as fast evolving technologies, most requirements cannot be planned a priori. To overcome this problem, we suggest a method to gather enduser needs for requirements engineers at any time by applying a geographical deployed feedback system. End-user needs are gathered in-situ by utilizing mobile devices. In this paper, we present the implementation of our feedback system enabling end-users to submit feedback with smartphones at very low effort and cost.
Mixed Cumulative Distribution Networks

Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately there are currently no good parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.
Prediction-Based Task Assignment in Spatial Crowdsourcing

With the rapid advancement of mobile devices and crowdsourcing platforms, spatial crowdsourcing has attracted much attention from various research communities. A spatial crowdsourcing system periodically matches a number of locationbased workers with nearby spatial tasks (e.g., taking photos or videos at some specific locations). Previous studies on spatial crowdsourcing focus on task assignment strategies that maximize an assignment score based solely on the available information about workers/tasks at the time of assignment. These strategies can only achieve local optimality by neglecting the workers/tasks that may join the system in a future time. In contrast, in this paper, we aim to improve the global assignment, by considering both present and future (via predictions) workers/tasks. In particular, we formalize a new optimization problem, namely maximum quality task assignment (MQA). The optimization objective of MQA is to maximize a global assignment quality score, under a traveling budget constraint. To tackle this problem, we design an effective grid-based prediction method to estimate the spatial distributions of workers/tasks in the future, and then utilize the predictions to assign workers to tasks at any given time instance. We prove that the MQA problem is NPhard, and thus intractable. Therefore, we propose efficient heuristics to tackle the MQA problem, including MQA greedy and MQA divide-and-conquer approaches, which can efficiently assign workers to spatial tasks with high quality scores and low budget consumptions. Through extensive experiments, we demonstrate the efficiency and effectiveness of our approaches on both real and synthetic datasets.
17 Microarray-Based Microbial Identification and Characterization

The development of molecular-based methodologies over the past two decades has dramatically improved our ability to detect microorganisms in clinical and environmental samples—enabling detection and identification within hours in many cases. However, most of these methods are only capable of monitoring individual or small groups of organisms at a time. Due to the extreme microbial diversity in many environments, such as the human intestine (Eckburg et al., 2005), it is necessary to monitor hundreds to thousands of different microbial populations simultaneously in order to detect all of the organisms of interest as a whole and understand these communities more comprehensively. Microarrays have the unprecedented potential to achieve this objective as specific, sensitive, quantitative, and high-throughput tools for microbial detection, identification, and characterization. Advances in printing technology have enabled the production of microarrays containing thousands to hundreds of thousands of probes. Although microarrays have been primarily developed and used for gene expression profiling of pure cultures of individual organisms, major advances have recently been made in their application to complex environmental samples. This chapter discusses the basis of different microarray formats and their application to issues of clinical interest. Several reviews on microarray technology have recently been published and may provide additional information of interest (Ye et al., 2001; Zhou and Thompson, 2002; Cook and Sayler, 2003; Zhou, 2003; Bodrossy and Sessitsch, 2004; Schadt and Zhou, 2005; Schadt et al., 2005).
Immersive BCI with SSVEP in VR head-mounted display

In this paper we present an immersive brain computer interface (BCI) where we use a virtual reality head-mounted display (VRHMD) to invoke SSVEP responses. Compared to visual stimuli in monitor display, we demonstrate that visual stimuli in VRHMD indeed improve the user engagement for BCI. To this end, we validate our method with experiments on a VR maze game, the goal of which is to guide a ball into the destination in a 2D grid map in a 3D space, successively choosing one of four neighboring cells using SSVEP evoked by visual stimuli on neighboring cells. Experiments indicate that the averaged information transfer rate is improved by 10% for VRHMD, compared to the case in monitor display and the users feel easier to play the game with the proposed system.
Learning Kernel-Based Halfspaces with the 0-1 Loss

We describe and analyze a new algorithm for agnostically learning kernel-based halfspaces with respect to the 0-1 loss function. Unlike most of the previous formulations, which rely on surrogate convex loss functions (e.g., hinge-loss in support vector machines (SVMs) and log-loss in logistic regression), we provide finite time/sample guarantees with respect to the more natural 0-1 loss function. The proposed algorithm can learn kernel-based halfspaces in worst-case time poly$(\exp(L\log(L/\epsilon)))$, for any distribution, where $L$ is a Lipschitz constant (which can be thought of as the reciprocal of the margin), and the learned classifier is worse than the optimal halfspace by at most $\epsilon$. We also prove a hardness result, showing that under a certain cryptographic assumption, no algorithm can learn kernel-based halfspaces in time polynomial in $L$.
Evaluation of two monkey species (Macaca mulatta and Macaca fascicularis) as possible models for human Helicobacter pylori disease

Endoscopic, histologic, and microbiologic evaluations of 21 cynomolgus and 34 rhesus monkeys for naturally occurring Helicobacter pylori infection were done. H. pylori was never isolated from any cynomolgus monkey, but was found in 12 rhesus monkeys. A general correlation existed between a positive culture and a gastric inflammatory response. Inoculation challenges were then undertaken. Four cynomolgus and five rhesus monkeys received two different H. pylori strains isolated from humans. Five rhesus monkeys received an isolate obtained from rhesus monkeys. Evaluation of the cynomolgus monkeys 7 and 14 days later revealed no H. pylori. Endoscopies of the rhesus monkeys were done 7, 14, 21, 28, and 56 days later. One rhesus monkey, which received the isolate from humans, became H. pylori positive at day 21 and remained positive through day 56. Restriction enzyme analysis of genomic DNA at day 56 revealed that the isolate was not identical to the challenge strain isolated from humans. All five rhesus monkeys that received the strain isolated from rhesus monkeys became H. pylori positive by day 14 and remained positive through day 56 Antral inflammation developed in all monkeys. Restriction enzyme analysis of genomic DNA on day 56 confirmed that four of five isolates were identical to the challenge strain isolated from rhesus monkeys. DNA hybridization documented homology between the challenge strains isolated from humans and rhesus monkeys plus those isolated at day 56. In this study, we showed that the rhesus monkey, if given a strain of H. pylori isolated from rhesus monkeys, develops a gastric infection with accompanying histological changes, making this model suitable for further development.
Solving POMDPs: RTDP-Bel vs. Point-based Algorithms

Point-based algorithms and RTDP-Bel are approximate methods for solving POMDPs that replace the full updates of parallel value iteration by faster and more effective updates at selected beliefs. An important difference between the two methods is that the former adopt Sondik's representation of the value function, while the latter uses a tabular representation and a discretization function. The algorithms, however, have not been compared up to now, because they target different POMDPs: discounted POMDPs on the one hand, and Goal POMDPs on the other. In this paper, we bridge this representational gap, showing how to transform discounted POMDPs into Goal POMDPs, and use the transformation to compare RTDP-Bel with point-based algorithms over the existing discounted benchmarks. The results appear to contradict the conventional wisdom in the area showing that RTDP-Bel is competitive, and sometimes superior to point-based algorithms in both quality and time.
Volunteer Cloud Computing: MapReduce over the Internet

Volunteer Computing harnesses computing resources of machines from around the world to perform distributed independent tasks, following a master/worker model. Despite the recent increase in popularity and power in middleware such as BOINC, there are still several limitations in existing systems. Current research is oriented towards optimizing existing applications, while the number of active users and projects has reached a plateau. A programming paradigm that has been significantly popular and is used by several systems on the cloud is MapReduce. The main advantage of this paradigm is that it can be used to solve a vast amount of different problems, by breaking them into simple steps and taking advantage of distributed resources. Volunteer Computing provides these resources, and although it cannot match the conditions offered by a cluster, it has other advantages that can be leveraged. In this paper, we try to increase the computational power of Volunteer Computing systems by allowing more complex applications and paradigms such as MapReduce to be run, thus opening new avenues and possibilities for the use of computational devices scattered through the Internet. We created a BOINC prototype that can run MapReduce jobs (BOINC-MR), using a pull-model in which communication is always initiated by the client. By running experiments on a small cluster, with multiple variables, we were able to evaluate a few initial scenarios with this paradigm. We used a simple MapReduce application, word count, as proof of concept, just to demonstrate a typical execution.
Data reduction techniques and hypothesis testing for analysis of benchmarking data

This paper proposes a data reduction and hypothesis testing methodology that can be used to perform hypothesis testing with data commonly collected in benchmarking studies. A reduced-form performance vector and reduced-form set of decision variables are constructed using the multivariate data reduction techniques of principal component analysis and exploratory factor analysis. Reductions in dependent and exogenous variables increase the available degrees of freedom, thereby facilitating the use of standard regression techniques. We demonstrate the methodology with data from a semiconductor production benchmarking study.
Effective Arterial Elastance as Index of Arterial Vascular Load in Humans

BackgroundThis study tested whether the simple ratio of ventricular end-systolic pressure to stroke volume, known as the effective arterial elastance (Ea), provides a valid measure of arterial load in humans with normal and aged hypertensive vasculatures. Methods and ResultsVentricular pressure-volume and invasive aortic pressure and flow were simultaneously determined in 10 subjects (four young normotensive and six older hypertensive). Measurements were obtained at rest, during mechanically reduced preload, and after pharmacological interventions. Two measures of arterial load were compared: One was derived from aortic input impedance and arterial compliance data using an algebraic expression based on a three-element Windkessel model of the arterial system [Ea(Z)I, and the other was more simply measured as the ratio of ventricular end-systolic pressure to stroke volume [Ea(PV)]. Although derived from completely different data sources and despite the simplifying assumptions of Ea(PV), both Ea(Z) and Ea(PV) were virtually identical over a broad range of altered conditions: Ea(PV) = 0.97 · Ea(Z) +0.17; n = 33, r2 = 0.98, SEE = 0.09, p < 0.0001. Whereas Ea(PV) also correlated with mean arterial resistance, it exceeded resistance by as much as 25% in older hypertensive subjects (because of reduced compliance and wave reflections), which better indexed the arterial load effects on the ventricle. Simple methods to estimate Ea (PV) from routine arterial pressures were tested and validated. ConclusionsEa(PV) provides a convenient, useful method to assess arterial load and its impact on the human ventricle. These results highlight effects of increased pulsatile load caused by aging or hypertension on the pressure-volume loop and indicate that this load and its effects on cardiac performance are often underestimated by mean arterial resistance but are better accounted for by Ea.
Vigorous Star Formation with Low Efficiency in Massive Disk Galaxies at z = 1.5

We present the first detection of molecular gas cooling CO emission lines from ordinary massive galaxies at z = 1.5. Two sources were observed with the IRAM Plateau de Bure Interferometer, selected to lie in the mass-star formation rate correlation at their redshift, thus being representative of massive high-z galaxies. Both sources were detected with high confidence, yielding L′CO ∼ 2 × 1010 K km s−1 pc2. For one of the sources we find evidence of velocity shear, implying CO sizes of ~10 kpc. With an infrared luminosity of LFIR ∼ 1012 L☉, these disklike galaxies are borderline ULIRGs but with star formation efficiency similar to that of local spirals, and an order of magnitude lower than that in submillimeter galaxies. This suggests a CO to total gas conversion factor similar to local spirals, gas consumption timescales approaching 1 Gyr or longer, and molecular gas masses reaching ~1011 M☉, comparable to or larger than the estimated stellar masses. These results support a major role of in situ gas consumption over cosmological timescales and with relatively low star formation efficiency, analogous to that of local spiral disks, for the formation of today’s most massive galaxies and their central black holes. Given the high space density of similar galaxies, ~10−4 Mpc−3, this implies a widespread presence of gas-rich galaxies in the early universe, many of which might be within reach of detailed investigations of current and planned facilities.