Statistical Iterative Methods Research Articles

Statistical Beamforming for Multi-Set Space–Time Shift-Keying-Based Full-Duplex Millimeter Wave Communications

Full-duplex (FD) communication has been shown to provide an increased achievable rate, while millimeter wave (mmWave) communications benefit from a large available bandwidth that further improves the achievable rate. On the other hand, the concept of multi-set space-time shift keying (MS-STSK) has been proposed to provide a flexible design trade-off between throughput and performance. Hence, in this work, we consider the design of an FD-aided MS-STSK transceiver for millimeter wave communications. However, a major challenge is that channel reciprocity is not valid in mmWave communications due to shorter channel coherence time. Thus, the uplink (UL) pilots cannot be utilized to estimate the downlink (DL) channel. To overcome this challenge, we propose a beamforming technique based on channel statistics without assuming channel reciprocity. For this purpose, a closed-form expression for the outage probability of the system is derived by employing the characterization of the ratio of the Indefinite Quadratic Form (IQF). The derived analytical expression is then utilized to design optimum beamforming weights using the Sequential Quadratic Programming (SQP)-based heuristic method. Moreover, an Iterative Statistical Method (ISM) of joint transmit and receive beamforming algorithm is also developed by utilizing Principle Eigenvector (PE) and Generalized Rayleigh Quotient (G-RQ) optimization techniques. Finally, we verify our simulation results with the theoretical analysis.

Statistical iterative spectral CT imaging method based on blind separation of polychromatic projections.

Spectral computed tomography (CT) can provide narrow-energy-width reconstructed images, thereby suppressing beam hardening artifacts and providing rich attenuation information for component characterization. We propose a statistical iterative spectral CT imaging method based on blind separation of polychromatic projections to improve the accuracy of narrow-energy-width image decomposition. For direct inversion in blind scenarios, we introduce the system matrix into the X-ray multispectral forward model to reduce indirect errors. A constrained optimization problem with edge-preserving regularization is established and decomposed into two sub-problems to be alternately solved. Experiments indicate that the novel algorithm obtains more accurate narrow-energy-width images than the state-of-the-art method.

Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)

In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalography. We consider a recently introduced hybrid reconstruction strategy combining a hierarchical Bayesian model to incorporate a priori information and the advanced randomized multiresolution scanning (RAMUS) source space decomposition approach to reduce modelling errors, respectively. In particular, we aim to generalize the previously extensively used conditionally Gaussian prior (CGP) formalism to achieve distributional reconstructions with higher focality. For this purpose, we introduce as a hierarchical prior, a general exponential distribution, which we refer to as conditionally exponential prior (CEP). The first-degree CEP corresponds to focality enforcing Laplace prior, but it also suffers from strong depth bias, when applied in numerical modelling, making the deep activity unrecoverable. We sample over multiple resolution levels via RAMUS to reduce this bias as it is known to depend on the resolution of the source space. Moreover, we introduce a procedure based on the physiological a priori knowledge of the brain activity to obtain the shape and scale parameters of the gamma hyperprior that steer the CEP. The posterior estimates are calculated using iterative statistical methods, expectation maximization and iterative alternating sequential algorithm, which we show to be algorithmically similar and to have a close resemblance to the iterative ell _1 and ell _2 reweighting methods. The performance of CEP is compared with the recent sampling-based dipole localization method Sequential semi-analytic Monte Carlo estimation (SESAME) in numerical experiments of simulated somatosensory evoked potentials related to the human median nerve stimulation. Our results obtained using synthetic sources suggest that a hybrid of the first-degree CEP and RAMUS can achieve an accuracy comparable to the second-degree case (CGP) while being more focal. Further, the proposed hybrid is shown to be robust to noise effects and compares well with the dipole reconstructions obtained with SESAME.

Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format

Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. Time-of-flight (TOF)-histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF-histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF-histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation-activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional expectation-maximization approach. A minimal difference was observed when the simulated TOF-histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF-histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware.

Gaussian noise level estimation for color image denoising.

Noise level is an important parameter in many visual applications, especially in image denoising. How to accurately estimate the noise level from a noisy image is a challenging problem. However, for color image denoising, it is not that the more accurate the noise level is, the better the denoising performance is, but that the noise level higher than the true noise can achieve a better denoising result. For better denoising, we propose a statistical iterative method based on low-rank image patches. We select the low-rank patches in the image and calculate the eigenvalues of the covariance matrix of these patches. Unlike the existing methods that take the smallest eigenvalue as the estimated noise level, the proposed method analyzes the relationship between the median value and the mean value of the eigenvalue according to the statistical property and selects an appropriate number of eigenvalues to average as the estimated noise level. Extensive experiments are conducted, demonstrating that our estimated noise level reaches the highest value of all comparison methods. And the denoising results on color images of our method outperform all the state-of-the-art methods and the true noise level.

Confidence in Inactive and Active Predictions from Structural Alerts.

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

Performance study of a directional fast neutron detection system based on TPC and plastic scintillation detectors

A neutron time projection chamber can locate the approximate direction of a neutron hot spot with high efficiency and a 4π field of view. The angular resolution can be significantly improved by adding several plastic scintillation detectors and using coincidence events. The specific performances of such a coincidence imaging system are studied based on theoretical calculations and experimental results. The calculated value of the angular resolution is approximately 2̂ for the current system, which agrees well with the experimental results and sets an upper limit for the angular resolution of traditional back projection based online reconstruction methods. Although the statistical iterative method can breakthrough this limit and further improve the angular resolution, the time consumption is usually a problem. The coincidence imaging system can be further optimized for future applications based on the theoretical model.

Multicentre study to determine the Etest epidemiological cut-off values of antifungal drugs in Candida spp. and Aspergillus fumigatus species complex

ObjectivesTo determine the Etest-based epidemiological cut-off values (ECVs) for antifungal agents against the most frequent yeast and Aspergillus fumigatus species isolated in 12 French hospitals. MethodsFor each antifungal agent, the Etest MICs in yeast and A. fumigatus isolates from 12 French laboratories were retrospectively collected from 2004 to 2018. The ECVs were then calculated using the iterative statistical method with a 97.5% cut-off. ResultsForty-eight Etest ECVs were determined for amphotericin B, caspofungin, micafungin, anidulafungin, fluconazole, voriconazole, posaconazole and itraconazole, after pooling and analysing the MICs of 9654 Candida albicans, 2939 Candida glabrata SC, 1458 Candida parapsilosis SC, 1148 Candida tropicalis, 575 Candida krusei, 518 Candida kefyr, 241 Candida lusitaniae, 131 Candida guilliermondii and 1526 Aspergillus fumigatus species complex isolates. These ECVs were 100% concordant (identical or within one two-fold dilution) with the previously reported Etest-based ECVs (when available), and they were concordant in 76.1% of cases with the Clinical and Laboratory Standards Institute ECVs and in 81.6% of cases with the European Committee on Antimicrobial Susceptibility Testing ECVs. ConclusionsOn the basis of these and other previous results, we recommend the determination of method-dependent ECVs. Etest ECVs should not be used instead of breakpoints, but may be useful to identify non-wild-type isolates with potential resistance to antifungal agents, and to indicate that an isolate may not respond as expected to the standard treatment.

A THIRD GENERATION TOMOGRAPHY SYSTEM WITH FIFTEEN DETECTORS SIMULATED BY MONTE CARLO METHOD

This paper describes the Monte Carlo simulation, using MCNP4C, of a multichannel third generation tomography system containing a two radioactive sources 192I (316.5 – 468 KeV) and 137Cs (662 KeV), and a set of fifteen NaI(Tl) detectors, with dimensions of 1 inch diameter and 2 inches thick, in fan beam geometry, positioned diametrically opposite. Each detector moves 10 steps of 0,24o, totalizing 150 virtual detectors per projection, and then the system rotate 2 degrees. The Monte Carlo simulation was performed to evaluate the viability of this configuration. For this, a multiphase phantom containing polymethyl methacralate (PMMA ((r @ 1.19 g/cm3)), iron (r @ 7.874 g/cm3), aluminum (r @ 2.6989 g/cm3) and air (r @ 1.20479E-03 g/cm3) was simulated. The simulated number of histories was 1.1E+09 per projection and the tally used were the F8, which gives the pulse height of each detector. The data obtained by the simulation was used to reconstruct the simulated phantom using the statistical iterative Maximum Likelihood Estimation Method Technique (ML-EM) algorithm. Each detector provides a gamma spectrum of the sources, and a pulse height analyzer (PHA) of 10% on the 316.5 KeV and 662 KeV photopeaks was performed. This technique provides two reconstructed images of the simulated phantom. The reconstructed images provided high spatial resolution, and it is supposed that the temporal resolution (spending time for one complete revolution) is about 2.5 hours.

I179] SPECT/CT quantitative reconstruction techniques

The last years, the clinical interest in quantitative SPECT/CT imaging is increasing. Especially in the rapidly growing field of radionuclide therapy, quantitative imaging is essential to provide nuclear medicine physicians with accurate dosimetric data. As a result, manufacturers now focus on producing SPECT/CT systems that are intrinsically quantitative. SPECT quantification is more challenging than PET quantification. In fact, the correction for photon absorption is relatively straightforward to apply in PET. In addition, in SPECT, we need to consider a variety of radionuclides with different photon energies and energy windows, different collimators and the potential for drifts in performance due to the greater number of moving parts in a SPECT system. With SPECT/CT, the coregistered CT data provide accurate information on the density of the body tissues, which can be used in algorithms that correct for photons that have been Compton-scattered or attenuated within the body. State-of-the-art reconstruction techniques are based on statistical iterative methods such as the ordered-subset maximum-likelihood expectation maximization algorithm (OSEM). In this approach, the physical characteristics of the acquisition process can be completely modeled in the reconstruction. In this presentation, an overview of all factors affecting SPECT image quantification will be presented. The latest developments to achieve quantitative SPECT/CT reconstruction will be discussed, together with calibration issues to finally obtain radioactivity concentrations per unit volume (kBq/ml).

Optimizing the atom types of proteins through iterative knowledge-based potentials**Project supported by the National Natural Science Foundation of China (Grant No. 31670724), the National Key Research and Development Program of China (Grant Nos. 2016YFC1305800 and 2016YFC1305805), and the Startup Grant of Huazhong University of Science and Technology, China.

Knowledge-based scoring functions have been widely used for protein structure prediction, protein–small molecule, and protein–nucleic acid interactions, in which one critical step is to find an appropriate representation of protein structures. A key issue is to determine the minimal protein representations, which is important not only for developing of scoring functions but also for understanding the physics of protein folding. Despite significant progresses in simplifying residues into alphabets, few studies have been done to address the optimal number of atom types for proteins. Here, we have investigated the atom typing issue by classifying the 167 heavy atoms of proteins through 11 schemes with 1 to 20 atom types based on their physicochemical and functional environments. For each atom typing scheme, a statistical mechanics-based iterative method was used to extract atomic distance-dependent potentials from protein structures. The atomic distance-dependent pair potentials for different schemes were illustrated by several typical atom pairs with different physicochemical properties. The derived potentials were also evaluated on a high-resolution test set of 148 diverse proteins for native structure recognition. It was found that there was a crossover around the scheme of four atom types in terms of the success rate as a function of the number of atom types, which means that four atom types may be used when investigating the basic folding mechanism of proteins. However, it was revealed by a close examination of typical potentials that 14 atom types were needed to describe the protein interactions at atomic level. The present study will be beneficial for the development of protein related scoring functions and the understanding of folding mechanisms.

MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

BackgroundCarbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson’s disease, and Alzheimer’s disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures.ResultsBy manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing.ConclusionThis study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

Abstract 563: Quantitative EMT expression score for predicting survival outcome

Abstract Tumor aggressiveness and subsequent metastasis still remain as major limitations to curative treatments for cancer patients. The epithelial to mesenchymal transition (EMT) is a process that occurs naturally during embryogenesis and has been linked to metastasis in many cancer types. EMT is characterized by phenotypic changes that allow for tissue extravasation and migration of cancer cells into the bloodstream. These changes may be linked to specific patterns in the gene expression signature of cells undergoing EMT. Recent theoretical efforts have predicted the existence of a stable, hybrid (E/M) phenotype which has also been observed experimentally in single cells. However, the effects of this hybrid phenotype on cancer patient survival have not been well characterized. The ability to quantify a patient’s EMT status via a simple test involving a small collection of prognostic genes would provide an important tool for clinical risk stratification and treatment in the context of many cancer types. Here, we apply iterative statistical methods to generate an EMT score based on gene expression samples. We use this score to characterize the degree of the hybrid phenotype signature present in test samples. Predictions from our model are verified against cell lines with known EMT status. Lastly, we apply our model to clinical samples in order to assess survival differences in various EMT groups. We demonstrate that in many cases, EMT status successfully classifies patients into groups with statistically significant differences in survival, which is of immediate clinical relevance. Citation Format: Jason T. George, Mohit K. Jolly, Herbert Levine. Quantitative EMT expression score for predicting survival outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 563. doi:10.1158/1538-7445.AM2017-563

$$\nu $$ ν acceleration of statistical iterative methods for image restoration

Image restoration problem is an important topic which appears in many different scientific areas. Several solving techniques are available, but generally in real applications, from which large-scale linear systems arise, the choice falls on iterative algorithms. In particular statistical methods (Lucy–Richardson method, image space reconstruction algorithm) have been extensively studied in the literature. Since they have low convergence rates, it is necessary to employ acceleration strategies. At this time, the most popular is the one introduced in 1997 by Biggs and Andrews, called automatic acceleration by the authors. In the present paper we describe \(\nu \) acceleration, that is the translation of the idea behind \(\nu \)-method, conceived for speeding up Landweber method, in the context of statistical methods. Computational results, which compare accelerated and classical methods, show the effectiveness of this strategy, which is able to get better performance than automatic acceleration.

Multichannel DEM reconstruction method based on Markov random fields for bistatic SAR

It appears very interesting and particularly useful to perform DEM reconstruction with the config- uration of multichannel bistatic SAR (BiSAR) because of its considerable advantages. In typical processing flow of multichannel interferometric synthetic aperture radar (InSAR), techniques based on statistical methods or iterative methods does not work very well with limits of specific BiSAR configurations. Moreover, the tradeoff between the correctness and precision of the reconstruction of different baselines needs to be overcome as well. Based on the advantages of multichannel BiSAR configuration, basic idea is the joint utilization of amplitude data, iterative reconstruction results and ML results to help the optimization of the total MRF energy function. Thus, a multichannel DEM reconstruction method based on Markov Random Fields (MRFs) for BiSAR, which concerns much about discontinuities and energy functions, is proposed to achieve not only correct but also more precise results. Afterward, the strategy to determine the weight parameter and processing flow is presented. Finally, simulated and real data experiments are given to validate the proposed method.

Compressed-sensing (CS)-based digital breast tomosynthesis (DBT) reconstruction for low-dose, accurate 3D breast X-ray imaging

In practical applications of three-dimensional (3D) tomographic techniques, such as digital breast tomosynthesis (DBT), computed tomography (CT), etc., there are often challenges for accurate image reconstruction from incomplete data. In DBT, in particular, the limited-angle and few-view projection data are theoretically insufficient for exact reconstruction; thus, the use of common filtered-backprojection (FBP) algorithms leads to severe image artifacts, such as the loss of the average image value and edge sharpening. One possible approach to alleviate these artifacts may employ iterative statistical methods because they potentially yield reconstructed images that are in better accordance with the measured projection data. In this work, as another promising approach, we investigated potential applications to low-dose, accurate DBT imaging with a state-of-the-art reconstruction scheme based on compressed-sensing (CS) theory. We implemented an efficient CS-based DBT algorithm and performed systematic simulation works to investigate the imaging characteristics. We successfully obtained DBT images of substantially very high accuracy by using the algorithm and expect it to be applicable to developing the next-generation 3D breast X-ray imaging system.

Evaluation of analytical reconstruction with a new gap-filling method in comparison to iterative reconstruction in [ $$_{ }^{11}\mathrm C$$ 11 C ]-raclopride PET studies

The aim of the study was (1) to evaluate the reconstruction strategies with dynamic [¹¹C]-raclopride human positron emission tomography (PET) studies acquired from ECAT high-resolution research tomograph (HRRT) scanner and (2) to justify for the selected gap-filling method for analytical reconstruction with simulated phantom data. A new transradial bicubic interpolation method has been implemented to enable faster analytical 3D-reprojection (3DRP) reconstructions for the ECAT HRRT PET scanner data. The transradial bicubic interpolation method was compared to the other gap-filling methods visually and quantitatively using the numerical Shepp-Logan phantom. The performance of the analytical 3DRP reconstruction method with this new gap-filling method was evaluated in comparison with the iterative statistical methods: ordinary Poisson ordered subsets expectation maximization (OPOSEM) and resolution modeled OPOSEM methods. The image reconstruction strategies were evaluated using human data at different count statistics and consequently at different noise levels. In the assessments, 14 [¹¹C]-raclopride dynamic PET studies (test-retest studies of 7 healthy subjects) acquired from the HRRT PET scanner were used. Besides the visual comparisons of the methods, we performed regional quantitative evaluations over the cerebellum, caudate and putamen structures. We compared the regional time-activity curves (TACs), areas under the TACs and binding potential (BPND) values. The results showed that the new gap-filling method preserves the linearity of the 3DRP method. Results with the 3DRP after gap-filling method exhibited hardly any dependency on the count statistics (noise levels) in the sinograms while we observed changes in the quantitative results with the EM-based methods for different noise contamination in the data. With this study, we showed that 3DRP with transradial bicubic gap-filling method is feasible for the reconstruction of high-resolution PET data with missing sinogram bins. The calculated intraclass correlation coefficient (ICC) values were similar for all tested methods and validated the test-retest study. The gap-filling and 3DRP method can be used for quantitative PET studies in which high temporal information is crucial and can serve as a reference method for comparison studies of the other reconstruction methods.

A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method

Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.

Expectation maximization for hard X-ray count modulation profiles

This paper is concerned with the image reconstruction problem when the measured data are solar hard X-ray modulation profiles obtained from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)} instrument. Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized for the analysis of count modulation profiles in solar hard X-ray imaging based on Rotating Modulation Collimators. The algorithm described in this paper solves the maximum likelihood problem iteratively and encoding a positivity constraint into the iterative optimization scheme. The result is therefore a classical Expectation Maximization method this time applied not to an image deconvolution problem but to image reconstruction from count modulation profiles. The technical reason that makes our implementation particularly effective in this application is the use of a very reliable stopping rule which is able to regularize the solution providing, at the same time, a very satisfactory Cash-statistic (C-statistic). The method is applied to both reproduce synthetic flaring configurations and reconstruct images from experimental data corresponding to three real events. In this second case, the performance of Expectation Maximization, when compared to Pixon image reconstruction, shows a comparable accuracy and a notably reduced computational burden; when compared to CLEAN, shows a better fidelity with respect to the measurements with a comparable computational effectiveness. If optimally stopped, Expectation Maximization represents a very reliable method for image reconstruction in the RHESSI context when count modulation profiles are used as input data.