Visualization of recombination intermediates produced by RAD52-mediated single-strand annealing.

Double-strand breaks (DSBs) occur frequently during DNA replication. They are also caused by ionizing radiation, chemical damage or as part of the series of programmed events that occur during meiosis. In yeast, DSB repair requires RAD52, a protein that plays a critical role in homologous recombination. Here we describe the actions of human RAD52 protein in a model system for single-strand annealing (SSA) using tailed (i.e. exonuclease resected) duplex DNA molecules. Purified human RAD52 protein binds resected DSBs and promotes associations between complementary DNA termini. Heteroduplex intermediates of these recombination reactions have been visualized by electron microscopy, revealing the specific binding of multiple rings of RAD52 to the resected termini and the formation of large protein complexes at heteroduplex joints formed by RAD52-mediated annealing.

Hard instances of algorithms and proof systems

"Vegeu el resum a l'inici del document del fitxer adjunt."

Approximation algorithms for two-state anti-ferromagnetic spin systems on bounded degree graphs

In a seminal paper [10], Weitz gave a deterministic fully polynomial approximation scheme for counting exponentially weighted independent sets (which is the same as approximating the partition function of the hard-core model from statistical physics) in graphs of degree at most d, up to the critical activity for the uniqueness of the Gibbs measure on the innite d-regular tree. ore recently Sly [8] (see also [1]) showed that this is optimal in the sense that if here is an FPRAS for the hard-core partition function on graphs of maximum egree d for activities larger than the critical activity on the innite d-regular ree then NP = RP. In this paper we extend Weitz's approach to derive a deterministic fully polynomial approximation scheme for the partition function of general two-state anti-ferromagnetic spin systems on graphs of maximum degree d, up to the corresponding critical point on the d-regular tree. The main ingredient of our result is a proof that for two-state anti-ferromagnetic spin systems on the d-regular tree, weak spatial mixing implies strong spatial mixing. his in turn uses a message-decay argument which extends a similar approach proposed recently for the hard-core model by Restrepo et al [7] to the case of general two-state anti-ferromagnetic spin systems.

Oncology clinicians' defenses and adherence to communication skills training with simulated patients: an exploratory study.

The aim of this exploratory study was to assess the impact of clinicians' defense mechanisms-defined as self-protective psychological mechanisms triggered by the affective load of the encounter with the patient-on adherence to a communication skills training (CST). The population consisted of oncology clinicians (N = 31) who participated in a CST. An interview with simulated cancer patients was recorded prior and 6 months after CST. Defenses were measured before and after CST and correlated with a prototype of an ideally conducted interview based on the criteria of CST-teachers. Clinicians who used more adaptive defense mechanisms showed better adherence to communication skills after CST than clinicians with less adaptive defenses (F(1, 29) = 5.26, p = 0.03, d = 0.42). Improvement in communication skills after CST seems to depend on the initial levels of defenses of the clinician prior to CST. Implications for practice and training are discussed. Communication has been recognized as a central element of cancer care [1]. Ineffective communication may contribute to patients' confusion, uncertainty, and increased difficulty in asking questions, expressing feelings, and understanding information [2, 3], and may also contribute to clinicians' lack of job satisfaction and emotional burnout [4]. Therefore, communication skills trainings (CST) for oncology clinicians have been widely developed over the last decade. These trainings should increase the skills of clinicians to respond to the patient's needs, and enhance an adequate encounter with the patient with efficient exchange of information [5]. While CSTs show a great diversity with regard to their pedagogic approaches [6, 7], the main elements of CST consist of (1) role play between participants, (2) analysis of videotaped interviews with simulated patients, and (3) interactive case discussion provided by participants. As recently stated in a consensus paper [8], CSTs need to be taught in small groups (up to 10-12 participants) and have a minimal duration of at least 3 days in order to be effective. Several systematic reviews evaluated the impact of CST on clinicians' communication skills [9-11]. Effectiveness of CST can be assessed by two main approaches: participant-based and patient-based outcomes. Measures can be self-reported, but, according to Gysels et al. [10], behavioral assessment of patient-physician interviews [12] is the most objective and reliable method for measuring change after training. Based on 22 studies on participants' outcomes, Merckaert et al. [9] reported an increase of communication skills and participants' satisfaction with training and changes in attitudes and beliefs. The evaluation of CST remains a challenging task and variables mediating skills improvement remain unidentified. We recently thus conducted a study evaluating the impact of CST on clinicians' defenses by comparing the evolution of defenses of clinicians participating in CST with defenses of a control group without training [13]. Defenses are unconscious psychological processes which protect from anxiety or distress. Therefore, they contribute to the individual's adaptation to stress [14]. Perry refers to the term "defensive functioning" to indicate the degree of adaptation linked to the use of a range of specific defenses by an individual, ranging from low defensive functioning when he or she tends to use generally less adaptive defenses (such as projection, denial, or acting out) to high defensive functioning when he or she tends to use generally more adaptive defenses (such as altruism, intellectualization, or introspection) [15, 16]. Although several authors have addressed the emotional difficulties of oncology clinicians when facing patients and their need to preserve themselves [7, 17, 18], no research has yet been conducted on the defenses of clinicians. For example, repeated use of less adaptive defenses, such as denial, may allow the clinician to avoid or reduce distress, but it also diminishes his ability to respond to the patient's emotions, to identify and to respond adequately to his needs, and to foster the therapeutic alliance. Results of the above-mentioned study [13] showed two groups of clinicians: one with a higher defensive functioning and one with a lower defensive functioning prior to CST. After the training, a difference in defensive functioning between clinicians who participated in CST and clinicians of the control group was only showed for clinicians with a higher defensive functioning. Some clinicians may therefore be more responsive to CST than others. To further address this issue, the present study aimed to evaluate the relationship between the level of adherence to an "ideally conducted interview", as defined by the teachers of the CST, and the level of the clinician' defensive functioning. We hypothesized that, after CST, clinicians with a higher defensive functioning show a greater adherence to the "ideally conducted interview" than clinicians with a lower defensive functioning.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Performance of an ICU ventilator and two turbin-based ventilators dedicated to non invasive ventilation (NIV) in simulated high inspiratory effort and rate: a NIV-bench-study.

INTRODUCTION. The role of turbine-based NIV ventilators (TBV) versus ICU ventilators with NIV mode activated (ICUV) to deliver NIV in case of severe respiratory failure remains debated. OBJECTIVES. To compare the response time and pressurization capacity of TBV and ICUV during simulated NIV with normal and increased respiratory demand, in condition of normal and obstructive respiratory mechanics. METHODS. In a two-chamber lung model, a ventilator simulated normal (P0.1 = 2 mbar, respiratory rate RR = 15/min) or increased (P0.1 = 6 mbar, RR = 25/min) respiratory demand. NIV was simulated by connecting the lung model (compliance 100 ml/mbar; resistance 5 or 20 l/mbar) to a dummy head equipped with a naso-buccal mask. Connections allowed intentional leaks (29 ± 5 % of insufflated volume). Ventilators to test: Servo-i (Maquet), V60 and Vision (Philips Respironics) were connected via a standard circuit to the mask. Applied pressure support levels (PSL) were 7 mbar for normal and 14 mbar for increased demand. Airway pressure and flow were measured in the ventilator circuit and in the simulated airway. Ventilator performance was assessed by determining trigger delay (Td, ms), pressure time product at 300 ms (PTP300, mbar s) and inspiratory tidal volume (VT, ml) and compared by three-way ANOVA for the effect of inspiratory effort, resistance and the ventilator. Differences between ventilators for each condition were tested by oneway ANOVA and contrast (JMP 8.0.1, p\0.05). RESULTS. Inspiratory demand and resistance had a significant effect throughout all comparisons. Ventilator data figure in Table 1 (normal demand) and 2 (increased demand): (a) different from Servo-i, (b) different from V60.CONCLUSION. In this NIV bench study, with leaks, trigger delay was shorter for TBV with normal respiratory demand. By contrast, it was shorter for ICUV when respiratory demand was high. ICUV afforded better pressurization (PTP 300) with increased demand and PSL, particularly with increased resistance. TBV provided a higher inspiratory VT (i.e., downstream from the leaks) with normal demand, and a significantly (although minimally) lower VT with increased demand and PSL.

UOC Algorithms

Aplicació per a iPad a mode de repositori de continguts relacionats amb l'ensenyament d'assignatures d'informàtica.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

Neonatal and adult ICU ventilators to provide ventilation in neonates, infants, and children: a bench model study.

BACKGROUND: Using a bench test model, we investigated the hypothesis that neonatal and/or adult ventilators equipped with neonatal/pediatric modes currently do not reliably administer pressure support (PS) in neonatal or pediatric patient groups in either the absence or presence of air leaks. METHODS: PS was evaluated in 4 neonatal and 6 adult ventilators using a bench model to evaluate triggering, pressurization, and cycling in both the absence and presence of leaks. Delivered tidal volumes were also assessed. Three patients were simulated: a preterm infant (resistance 100 cm H2O/L/s, compliance 2 mL/cm H2O, inspiratory time of the patient [TI] 400 ms, inspiratory effort 1 and 2 cm H2O), a full-term infant (resistance 50 cm H2O/L/s, compliance 5 mL/cm H2O, TI 500 ms, inspiratory effort 2 and 4 cm H2O), and a child (resistance 30 cm H2O/L/s, compliance 10 mL/cm H2O, TI 600 ms, inspiratory effort 5 and 10 cm H2O). Two PS levels were tested (10 and 15 cm H2O) with and without leaks and with and without the leak compensation algorithm activated. RESULTS: Without leaks, only 2 neonatal ventilators and one adult ventilator had trigger delays under a given predefined acceptable limit (1/8 TI). Pressurization showed high variability between ventilators. Most ventilators showed TI in excess high enough to seriously impair patient-ventilator synchronization (> 50% of the TI of the subject). In some ventilators, leaks led to autotriggering and impairment of ventilation performance, but the influence of leaks was generally lower in neonatal ventilators. When a noninvasive ventilation algorithm was available, this was partially corrected. In general, tidal volume was calculated too low by the ventilators in the presence of leaks; the noninvasive ventilation algorithm was able to correct this difference in only 2 adult ventilators. CONCLUSIONS: No ventilator performed equally well under all tested conditions for all explored parameters. However, neonatal ventilators tended to perform better in the presence of leaks. These findings emphasize the need to improve algorithms for assisted ventilation modes to better deal with situations of high airway resistance, low pulmonary compliance, and the presence of leaks.

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption