Value of Real-World Evidence for Treatment Selection: A Case Study in Colon Cancer.

PURPOSE: Real-world evidence (RWE)-derived from analysis of real-world data (RWD)-has the potential to guide personalized treatment decisions. However, because of potential confounding, generating valid RWE is challenging. This study demonstrates how to responsibly generate RWE for treatment decisions. We validate our approach by demonstrating that we can uncover an existing adjuvant chemotherapy (ACT) guideline for stage II and III colon cancer (CC)-which came about using both data from randomized controlled trials and expert consensus-solely using RWD. METHODS: Data from the population-based Netherlands Cancer Registry from a total of 27,056 patients with stage II and III CC who underwent curative surgery were analyzed to estimate the overall survival (OS) benefit of ACT. Focusing on 5-year OS, the benefit of ACT was estimated for each patient using G-computation methods by adjusting for patient and tumor characteristics and estimated propensity score. Subsequently, on the basis of these estimates, an ACT decision tree was constructed. RESULTS: The constructed decision tree corresponds to the current Dutch guideline: patients with stage III or stage II with T stage 4 should receive surgery and ACT, whereas patients with stage II with T stage 3 should only receive surgery. Interestingly, we do not find sufficient RWE to conclude against ACT for stage II with T stage 4 and microsatellite instability-high (MSI-H), a recent addition to the current guideline. CONCLUSION: RWE, if used carefully, can provide a valuable addition to our construction of evidence on clinical decision making and therefore ultimately affect treatment guidelines. Next to validating the ACT decisions advised in the current Dutch guideline, this paper suggests additional attention should be paid to MSI-H in future iterations of the guideline.

Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform.

Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.

Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data.

BACKGROUND: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. OBJECTIVE: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. DESIGN, SETTING, AND PARTICIPANTS: From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. RESULTS AND LIMITATIONS: The most common comorbidities were hypertension (35-73%), obesity (9.2-54%), and type 2 diabetes (11-28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12-25%) and emergency department visits (10-14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. CONCLUSIONS: Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. PATIENT SUMMARY: Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.

Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports.

PURPOSE: Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common. We suggest 2 methods to leverage the knowledge encoded in pre-existing rules to inform ML decisions and obtain high performance, even with scarce annotations. METHODS: We collected 501 prostate pathology reports from 6 American hospitals. Reports were split into 2,711 core segments, annotated with 20 attributes describing the histology, grade, extension, and location of tumors. The data set was split by institutions to generate a cross-institutional evaluation setting. We assessed 4 systems, namely a rule-based approach, an ML model, and 2 hybrid systems integrating the previous methods: a Rule as Feature model and a Classifier Confidence model. Several ML algorithms were tested, including logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGB). RESULTS: When training on data from a single institution, LR lags behind the rules by 3.5% (F1 score: 92.2% v 95.7%). Hybrid models, instead, obtain competitive results, with Classifier Confidence outperforming the rules by +0.5% (96.2%). When a larger amount of data from multiple institutions is used, LR improves by +1.5% over the rules (97.2%), whereas hybrid systems obtain +2.2% for Rule as Feature (97.7%) and +2.6% for Classifier Confidence (98.3%). Replacing LR with SVM or XGB yielded similar performance gains. CONCLUSION: We developed methods to use pre-existing handcrafted rules to inform ML algorithms. These hybrid systems obtain better performance than either rules or ML models alone, even when training data are limited.

Comparison between Conventional IMRT Planning and a Novel Real-Time Adaptive Planning Strategy in Hypofractionated Regimes for Prostate Cancer: A Proof-of-Concept Planning Study.

In prostate cancer external beam radiation therapy (EBRT), intra-fraction prostate drifts may compromise the treatment efficacy by underdosing the target and/or overdosing the organs at risk. In this study, a recently developed real-time adaptive planning strategy for intensity-modulated radiation therapy (IMRT) for prostate cancer was evaluated in hypofractionated regimes against traditional treatment planning based on a treatment volume margin expansion. The proposed workflow makes use of a "library of plans" corresponding to possible intra-fraction prostate positions. During delivery, at each beam end, the plan prepared for the position of the prostate closest to the current one is selected and the corresponding beam delivered. This adaptive planning strategy was compared with the traditional approach on a clinical prostate cancer case where different prostate shift magnitudes were considered. Five, six and fifteen fraction hypofractionated schemes were considered for each of these scenarios. When shifts larger than the treatment margin were present, using the traditional approach the seminal vesicles were underdosed by 3-4% of the prescribed dose. The adaptive approach instead allowed for correct target dose coverage and lowered the dose on the rectum for each dosimetric endpoint on average by 3-4% in all the fractionation schemes. Standard intensity-modulated radiation therapy planning did not always guarantee a correct dose distribution on the seminal vesicles and the rectum. The adaptive planning strategy proposed resulted insensitive to the intra-fraction prostate drifts, produced a dose distribution in agreement with the dosimetric requirements in every case analysed and significantly lowered the dose on the rectum.

High correlation between radiation dose estimates for 256-slice CT obtained by highly parallelized hybrid Monte Carlo computation and solid-state metal-oxide semiconductor field-effect transistor measurements in physical anthropomorphic phantoms.

PURPOSE: Accurate, patient-specific radiation dosimetry for CT scanning is critical to optimize radiation doses and balance dose against image quality. While Monte Carlo (MC) simulation is often used to estimate doses from CT, comparison of estimates to experimentally measured values is lacking for advanced CT scanners incorporating novel design features. We aimed to compare radiation dose estimates from MC simulation to doses measured in physical anthropomorphic phantoms using metal-oxide semiconductor field-effect transistors (MOSFETs) in a 256-slice CT scanner. METHODS: Fifty MOSFETs were placed in organs within tissue-equivalent anthropomorphic adult and pediatric radiographic phantoms, which were scanned using a variety of chest, cardiac, abdomen, brain, and whole-body protocols on a 256-slice system. MC computations were performed on voxelized CT reconstructions of the phantoms using a highly parallel MC tool developed specifically for diagnostic X-ray energies and rapid computation. Doses were compared between MC estimates and physical measurements. RESULTS: The average ratio of MOSFET to MC dose in the in-field region was close to 1 (range, 0.96-1.12; mean ± SD, 1.01 ± 0.04), indicating outstanding agreement between measured and simulated doses. The difference between measured and simulated doses tended to increase with distance from the in-field region. The error in the MC simulations due to the limited number of simulated photons was less than 1%. The errors in the MOSFET dose determinations in the in-field region for a single scan were mainly due to the calibration method and were typically about 6% (8% if the error in the reading of the ionization chamber that was used for the MOSFET calibration was included). CONCLUSIONS: Radiation dose estimation using a highly parallelized MC method is strongly correlated with experimental measurements in physical adult and infant anthropomorphic phantoms for a wide range of scans performed on a 256-slice CT scanner. Incorporation into CT scanners of radiation-dose distribution estimation, employing the scanner's reconstructed images of the patient, may offer the potential for accurate patient-specific CT dosimetry.

Negation Scope Detection in Clinical Notes and Scientific Abstracts: A Feature-enriched LSTM-based Approach.

Electronic Health Records contain a wealth of clinical information that can potentially be used for a variety of clinical tasks. Clinical narratives contain information about the existence or absence of medical conditions as well as clinical findings. It is essential to be able to distinguish between the two since the negated events and the non-negated events often have very different prognostic value. In this paper, we present a feature-enriched neural network-based model for negation scope detection in biomedical texts. The system achieves a robust high performance on two different types of texts, scientific abstracts, and radiology reports, achieving the new state-of-the-art result without requiring the availability of gold cue information for negation scope detection task on the scientific abstracts part of BioScope1 corpus and competitive result on the radiology report corpus.

Real-time adaptive planning method for radiotherapy treatment delivery for prostate cancer patients, based on a library of plans accounting for possible anatomy configuration changes.

BACKGROUND AND PURPOSE: In prostate cancer treatment with external beam radiation therapy (EBRT), prostate motion and internal changes in tissue distribution can lead to a decrease in plan quality. In most currently used planning methods, the uncertainties due to prostate motion are compensated by irradiating a larger treatment volume. However, this could cause underdosage of the treatment volume and overdosage of the organs at risk (OARs). To reduce this problem, in this proof of principle study we developed and evaluated a novel adaptive planning method. The strategy proposed corrects the dose delivered by each beam according to the actual position of the target in order to produce a final dose distribution dosimetrically as similar as possible to the prescribed one. MATERIAL AND METHODS: Our adaptive planning method was tested on a phantom case and on a clinical case. For the first, a pilot study was performed on an in-silico pelvic phantom. A "library" of intensity modulated RT (IMRT) plans corresponding to possible positions of the prostate during a treatment fraction was generated at planning stage. Then a 3D random walk model was used to simulate possible displacements of the prostate during the treatment fraction. At treatment stage, at the end of each beam, based on the current position of the target, the beam from the library of plans, which could reproduce the best approximation of the prescribed dose distribution, was selected and delivered. In the clinical case, the same approach was used on two prostate cancer patients: for the first a tissue deformation was simulated in-silico and for the second a cone beam CT (CBCT) taken during the treatment was used to simulate an intra-fraction change. Then, dosimetric comparisons with the standard treatment plan and, for the second patient, also with an isocenter shift correction, were performed. RESULTS: For the phantom case, the plan generated using the adaptive planning method was able to meet all the dosimetric requirements and to correct for a misdosage of 13% of the dose prescription on the prostate. For the first clinical case, the standard planning method caused underdosage of the seminal vesicles, respectively by 5% and 4% of the prescribed dose, when the position changes for the target were correctly taken into account. The proposed adaptive planning method corrected any possible missed target coverage, reducing at the same time the dose on the OARs. For the second clinical case, both with the standard planning strategy and with the isocenter shift correction target coverage was significantly worsened (in particular uniformity) and some organs exceeded some toxicity objectives. While with our approach, the most uniform coverage for the target was produced and systematically the lowest toxicity values for the organs at risk were achieved. CONCLUSIONS: In our proof of principle study, the adaptive planning method performed better than the standard planning and the isocenter shift methods for prostate EBRT. It improved the coverage of the treatment volumes and lowered the dose to the OARs. This planning method is particularly promising for hypofractionated IMRT treatments in which a higher precision and control on dose deposition are needed. Further studies will be performed to test more extensively the proposed adaptive planning method and to evaluate it at a full clinical level.

Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning.

In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information. In this work, we propose an unsupervised machine learning approach to automatically structure radiology reports by detecting and normalizing anatomical phrases based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) ontology. The proposed approach combines word embedding-based semantic learning with ontology-based concept mapping to derive the desired concept normalization. The word embedding model was trained using a large corpus of unlabeled radiology reports. Fifty-six anatomical labels were extracted from SNOMED CT as class labels of the whole human anatomy. The proposed framework was compared against a number of state-of-the-art supervised and unsupervised approaches. Radiology reports from three different clinical sites were manually labeled for testing. The proposed approach outperformed other techniques yielding an average precision of 82.6%. The proposed framework boosts the coverage and performance of conventional approaches for concept normalization, by applying word embedding techniques in semantic learning, while avoiding the challenge of having access to a large amount of annotated data, which is typically required for training classifiers.

Calibration and error analysis of metal-oxide-semiconductor field-effect transistor dosimeters for computed tomography radiation dosimetry.

PURPOSE: Metal-oxide-semiconductor field-effect transistors (MOSFETs) serve as a helpful tool for organ radiation dosimetry and their use has grown in computed tomography (CT). While different approaches have been used for MOSFET calibration, those using the commonly available 100 mm pencil ionization chamber have not incorporated measurements performed throughout its length, and moreover, no previous work has rigorously evaluated the multiple sources of error involved in MOSFET calibration. In this paper, we propose a new MOSFET calibration approach to translate MOSFET voltage measurements into absorbed dose from CT, based on serial measurements performed throughout the length of a 100-mm ionization chamber, and perform an analysis of the errors of MOSFET voltage measurements and four sources of error in calibration. METHODS: MOSFET calibration was performed at two sites, to determine single calibration factors for tube potentials of 80, 100, and 120 kVp, using a 100-mm-long pencil ion chamber and a cylindrical computed tomography dose index (CTDI) phantom of 32 cm diameter. The dose profile along the 100-mm ion chamber axis was sampled in 5 mm intervals by nine MOSFETs in the nine holes of the CTDI phantom. Variance of the absorbed dose was modeled as a sum of the MOSFET voltage measurement variance and the calibration factor variance, the latter being comprised of three main subcomponents: ionization chamber reading variance, MOSFET-to-MOSFET variation and a contribution related to the fact that the average calibration factor of a few MOSFETs was used as an estimate for the average value of all MOSFETs. MOSFET voltage measurement error was estimated based on sets of repeated measurements. The calibration factor overall voltage measurement error was calculated from the above analysis. RESULTS: Calibration factors determined were close to those reported in the literature and by the manufacturer (~3 mV/mGy), ranging from 2.87 to 3.13 mV/mGy. The error σV of a MOSFET voltage measurement was shown to be proportional to the square root of the voltage V: σV=cV where c = 0.11 mV. A main contributor to the error in the calibration factor was the ionization chamber reading error with 5% error. The usage of a single calibration factor for all MOSFETs introduced an additional error of about 5-7%, depending on the number of MOSFETs that were used to determine the single calibration factor. The expected overall error in a high-dose region (~30 mGy) was estimated to be about 8%, compared to 6% when an individual MOSFET calibration was performed. For a low-dose region (~3 mGy), these values were 13% and 12%. CONCLUSIONS: A MOSFET calibration method was developed using a 100-mm pencil ion chamber and a CTDI phantom, accompanied by an absorbed dose error analysis reflecting multiple sources of measurement error. When using a single calibration factor, per tube potential, for different MOSFETs, only a small error was introduced into absorbed dose determinations, thus supporting the use of a single calibration factor for experiments involving many MOSFETs, such as those required to accurately estimate radiation effective dose.

Estimating Effective Dose of Radiation From Pediatric Cardiac CT Angiography Using a 64-MDCT Scanner: New Conversion Factors Relating Dose-Length Product to Effective Dose.

OBJECTIVE: The purpose of this study is to determine the conversion factors that enable accurate estimation of the effective dose (ED) used for cardiac 64-MDCT angiography performed for children. MATERIALS AND METHODS: Anthropomorphic phantoms representative of 1- and 10-year-old children, with 50 metal oxide semiconductor field-effect transistor dosimeters placed in organs, underwent scanning performed using a 64-MDCT scanner with different routine clinical cardiac scan modes and x-ray tube potentials. Organ doses were used to calculate the ED on the basis of weighting factors published in 1991 in International Commission on Radiological Protection (ICRP) publication 60 and in 2007 in ICRP publication 103. The EDs and the scanner-reported dose-length products were used to determine conversion factors for each scan mode. The effect of infant heart rate on the ED and the conversion factors was also assessed. RESULTS: The mean conversion factors calculated using the current definition of ED that appeared in ICRP publication 103 were as follows: 0.099 mSv · mGy-1 · cm-1, for the 1-year-old phantom, and 0.049 mSv · mGy-1 · cm-1, for the 10-year-old phantom. These conversion factors were a mean of 37% higher than the corresponding conversion factors calculated using the older definition of ED that appeared in ICRP publication 60. Varying the heart rate did not influence the ED or the conversion factors. CONCLUSION: Conversion factors determined using the definition of ED in ICRP publication 103 and cardiac, rather than chest, scan coverage suggest that the radiation doses that children receive from cardiac CT performed using a contemporary 64-MDCT scanner are higher than the radiation doses previously reported when older chest conversion factors were used. Additional up-to-date pediatric cardiac CT conversion factors are required for use with other contemporary CT scanners and patients of different age ranges.

The influence of DNA shape fluctuations on fluorescence resonance energy transfer efficiency measurements in nucleosomes.

Fluorescence resonance energy transfer (FRET) measurements allow one to observe site exposure in nucleosomes, i.e. the transient unwrapping of a part of the wrapped DNA from the histone octamer. In such experiments one can typically distinguish between a closed state and an open state but in principle one might hope to detect several states, each corresponding to a certain number of open binding sites. Here we show that even in an ideal FRET setup it would be hard to detect unwrapping states with intermediate levels of FRET efficiencies. As the unwrapped DNA molecule, modelled here as a wormlike chain, has a finite stiffness, shape fluctuations smear out FRET signals completely from such intermediate states.

The ends of a large RNA molecule are necessarily close.

We show on general theoretical grounds that the two ends of single-stranded (ss) RNA molecules (consisting of roughly equal proportions of A, C, G and U) are necessarily close together, largely independent of their length and sequence. This is demonstrated to be a direct consequence of two generic properties of the equilibrium secondary structures, namely that the average proportion of bases in pairs is ∼60% and that the average duplex length is ∼4. Based on mfold and Vienna computations on large numbers of ssRNAs of various lengths (1000-10 000 nt) and sequences (both random and biological), we find that the 5'-3' distance-defined as the sum of H-bond and covalent (ss) links separating the ends of the RNA chain-is small, averaging 15-20 for each set of viral sequences tested. For random sequences this distance is ∼12, consistent with the theory. We discuss the relevance of these results to evolved sequence complementarity and specific protein binding effects that are known to be important for keeping the two ends of viral and messenger RNAs in close proximity. Finally we speculate on how our conclusions imply indistinguishability in size and shape of equilibrated forms of linear and covalently circularized ssRNA molecules.

Nucleosome stability and accessibility of its DNA to proteins.

In this paper we present a theoretical description of the accessibility of nucleosomal DNA to proteins. We reassess the classical analysis of Polach and Widom (1995) who demonstrated that proteins (in their case restriction enzymes) gain access to buried binding sites inside a nucleosome through spontaneous unwrapping of DNA from the protein spool. We introduce a straightforward nucleosome model the predictions of which show good agreement with experimental data. By fitting the model to the data we obtain the values of two quantities: the adsorption energy to the histone octamer per length of DNA and the extra length that the DNA needs to unwrap beyond the binding site of an enzyme before the enzyme can act as effectively as on bare DNA. Our results indicate that the effective binding energy is surprisingly low which suggests that the nucleosomal parameters are tuned such that two large energies, the DNA bending energy and the pure adsorption energy, nearly cancel. This paper is based on a lecture presented at the summer school "DNA and Chromosomes 2009: Physical and Biological Applications". We follow the lecture as closely as possible which is why we spend more time than usual on issues that are already well-known in the field, and why we discuss some well-known results from a different perspective.

Multishell structures of virus coat proteins.

Under conditions of low ionic strength and a pH ranging between about 3.7 and 5.0, solutions of purified coat proteins of cowpea chlorotic mottle virus (CCMV) form spherical multishell structures in the absence of viral RNA. The outer surfaces of the shells in these structures are negatively charged, whereas the inner surfaces are positively charged due to a disordered cationic N-terminal domain of the capsid protein, the arginine-rich RNA-binding motif that protrudes into the interior. We show that the main forces stabilizing these multishells are counterion release combined with a lower charge density in the RNA-binding motif region of the outer shells due to their larger radii of curvature, arguing that these compensate for the outer shells not being able to adopt the smaller, optimal, radius of curvature of the inner shell. This explains why the structures are only stable at low ionic strengths at pHs for which the outer surface is negatively charged and why the larger outer shells are not observed separately in solution. We show how to calculate the free energy of shells of nonoptimal radius of curvature from the elastic properties of the native shell. The spacing between shells is determined mainly by the entropic elasticity of the RNA-binding motifs. Although we focus on CCMV multishells, we also predict the solution conditions under which multishells formed by CCMV coat protein mutants with a lower RNA-binding motif charge are stable, and we examine other viruses as well. We conclude that at a given surface charge density, the boundaries separating regions of stable multishells with different numbers of shells shift to lower ionic strengths upon either increasing the length of the RNA-binding motif, increasing the stiffness of the shells, or decreasing the charge per RNA-binding motif.

The force acting on a polymer partially confined in a tube.

We consider the force acting on a polymer part of whose length is configurationally confined in a tube and the rest of which is free. This situation arises in many different physical contexts, including a flexible synthetic polymer partially confined in a nanopore and a stiff viral genome partially ejected from its capsid. In both cases the force acting to pull the chain molecule out of its confinement is argued to be constant once a few persistence lengths are "free"/"outside". We present Brownian dynamics simulations that confirm the constancy of the force for different chain lengths and illustrate the dependence of the force on the strength of tube confinement. Experimental results are reported for genome ejection from viral capsids, from which we estimate the pulling force to be a few tenths of a piconewton.

Predicting the sizes of large RNA molecules.

We present a theory of the dependence on sequence of the three-dimensional size of large single-stranded (ss) RNA molecules. The work is motivated by the fact that the genomes of many viruses are large ssRNA molecules-often several thousand nucleotides long-and that these RNAs are spontaneously packaged into small rigid protein shells. We argue that there has been evolutionary pressure for the genome to have overall spatial properties-including an appropriate radius of gyration, R(g)-that facilitate this assembly process. For an arbitrary RNA sequence, we introduce the (thermal) average maximum ladder distance (MLD) and use it as a measure of the "extendedness" of the RNA secondary structure. The MLD values of viral ssRNAs that package into capsids of fixed size are shown to be consistently smaller than those for randomly permuted sequences of the same length and base composition, and also smaller than those of natural ssRNAs that are not under evolutionary pressure to have a compact native form. By mapping these secondary structures onto a linear polymer model and by using MLD as a measure of effective contour length, we predict the R(g) values of viral ssRNAs are smaller than those of nonviral sequences. More generally, we predict the average MLD values of large nonviral ssRNAs scale as N(0.67+/-0.01), where N is the number of nucleotides, and that their R(g) values vary as MLD(0.5) in an ideal solvent, and hence as N(0.34). An alternative analysis, which explicitly includes all branches, is introduced and shown to yield consistent results.

Collective diffusion coefficient of proteins with hydrodynamic, electrostatic, and adhesive interactions.

A theory is presented for lambdaC, the coefficient of the first-order correction in the density of the collective diffusion coefficient, for protein spheres interacting by electrostatic and adhesive forces. An extensive numerical analysis of the Stokesian hydrodynamics of two moving spheres is given so as to gauge the precise impact of lubrication forces. An effective stickiness is introduced and a simple formula for lambdaC in terms of this variable is put forward. A precise though more elaborate approximation for lambdaC is also developed. These and numerically exact expressions for lambdaC are compared with experimental data on lysozyme at pH 4.5 and a range of ionic strengths between 0.05M and 2M.

Application of the optimized Baxter model to the hard-core attractive Yukawa system.

We perform Monte Carlo simulations on the hard-core attractive Yukawa system to test the optimized Baxter model that was introduced by Prinsen and Odijk [J. Chem. Phys. 121, 6525 (2004)] to study a fluid phase of spherical particles interacting through a short-range pair potential. We compare the chemical potentials and pressures from the simulations with analytical predictions from the optimized Baxter model. We show that the model is accurate to within 10% over a range of volume fractions from 0.1 to 0.4, interaction strengths up to three times the thermal energy, and interaction ranges from 6% to 20% of the particle diameter, and performs even better in most cases. We furthermore establish the consistency of the model by showing that the thermodynamic properties of the Yukawa fluid computed via simulations may be understood on the basis of one similarity variable, the stickiness parameter defined within the optimized Baxter model. Finally, we show that the optimized Baxter model works significantly better than an often used, naive method determining the stickiness parameter by equating the respective second virial coefficients based on the attractive Yukawa and Baxter potentials.

Fluid-crystal coexistence for proteins and inorganic nanocolloids: Dependence on ionic strength.

We investigate theoretically the fluid-crystal coexistence of solutions of globular charged nanoparticles such as proteins and inorganic colloids. The thermodynamic properties of the fluid phase are computed via the optimized Baxter model P. Prinsen and T. Odijk [J. Chem. Phys. 121, 6525 (2004)]. This is done specifically for lysozyme and silicotungstates for which the bare adhesion parameters are evaluated via the experimental second virial coefficients. The electrostatic free energy of the crystal is approximated by supposing the cavities in the interstitial phase between the particles are spherical in form. In the salt-free case a Poisson-Boltzmann equation is solved to calculate the effective charge on a particle and a Donnan approximation is used to derive the chemical potential and osmotic pressure in the presence of salt. The coexistence data of lysozyme and silicotungstates are analyzed within this scheme, especially with regard to the ionic-strength dependence of the chemical potentials. The latter agree within the two phases provided some upward adjustment of the effective charge is allowed for.