Citations

Abstract Kelvin–Voigt fractional derivative (KVFD) model parameters have been used to describe viscoelastic properties of soft tissues. However, translating model parameters into a concise set of intrinsic mechanical properties related to tissue composition and structure remains challenging. This paper begins by exploring these relationships using a biphasic emulsion materials with known composition. Mechanical properties are measured by analyzing data from two indentation techniques—ramp-stress relaxation and load-unload hysteresis tests. Material composition is predictably correlated with viscoelastic model parameters. Model parameters estimated from the tests reveal that elastic modulus E 0 closely approximates the shear modulus for pure gelatin. Fractional-order parameter α and time constant τ vary monotonically with the volume fraction of the material’s fluid component. α characterizes medium fluidity and the rate of energy dissipation, and τ is a viscous time constant. Numerical simulations suggest that the viscous coefficient η is proportional to the energy lost during quasi-static force-displacement cycles, E A . The slope of E A versus η is determined by α and the applied indentation ramp time T r . Experimental measurements from phantom and ex vivo liver data show close agreement with theoretical predictions of the η − E A relation. The relative error is less than 20% for emulsions 22% for liver. We find that KVFD model parameters form a concise features space for biphasic medium characterization that described time-varying mechanical properties.

AbstractIn the past decades, a few synergistic feature selection algorithms have been published, which includes Cooperative Index (CI) and K‐Top Scoring Pair (k‐TSP). These algorithms consider the synergistic behavior of features when they are included in a feature panel. Although promising results have been shown for these algorithms, there is lack of a comprehensive and fair comparison with other feature selection algorithms across a large number of microarray datasets in terms of classification accuracy and computational complexity. There is a need in evaluating their performance and reducing the complexity of such algorithms. We compared the performance of synergistic feature selection algorithms with 11 other commonly used algorithms based on 22 microarray gene expression binary class datasets. The evaluation confirms that synergistic algorithms such as CI and k‐TSP will gradually increase the classification performance as more features are used in the classifiers. Also, in order to cut down computational cost, we proposed a new feature selection ranking score called Positive Synergy Index (PSI). Testing results show that features selected using PSI as well as synergistic feature selection algorithms provide better performance compared to with all other methods, while PSI has a computational complexity significantly lower than that of other synergistic algorithms.

A total of 136 subjects (51 SS patients, 35 sicca syndrome patients without SS, and 50 healthy volunteers) were enrolled in this study. The mean SWV value for salivary glands of SS patients was statistically higher than that of controls (2.81 ± 0.66 m/s versus 1.85 ± 0.28 m/s for parotid glands and 2.29 ± 0.34 m/s versus 1.82 ± 0.25 m/s for submandibular glands, resp.). Combining SWV values of parotid and submandibular glands gives a sensitivity of 88.2% (95% CI: 76.1–95.6%) and specificity of 96.0% (95% CI: 86.3–99.5%) at the cutoff point of 2.19 m/s, with an AUROC of 0.954 (95% CI: 0.893–0.986). In addition, combining SGUS score and SWV value yields a sensitivity of 98.0% (95% CI: 89.6–100%), specificity of 90.0% (95% CI: 78.2–96.7%), and AUROC of 0.962 (95% CI: 0.904–0.990). Classification tree considering the sequential use of SGUS score and SWV value achieved 92.1% accuracy for diagnosis of SS. Similarly, the ROC curve of combined SGUS scores and SWV values yields an AUROC of 0.954 (95% CI: 0.885–0.987), sensitivity of 97.1% (95% CI: 85.1–99.9%), and specificity of 92.2% (95% CI: 81.1–97.8%) for separating sicca syndrome patients (without SS) from SS patients. Combining SGUS and VTQ provides a promising tool for diagnosis of SS.

A novel protein complex facilitates lipid droplet autophagy.

Abstract Background The advancement of knowledge about control of antibiotic resistance depends on the rigorous evaluation of alternative intervention strategies. The STAR*ICU trial examined the effects of active surveillance and expanded barrier precautions on acquisition of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) in intensive care units. We report a reanalyses of the STAR*ICU trial using a Bayesian transmission modeling framework. Methods The data included admission and discharge times and surveillance test times and results. Markov chain Monte Carlo stochastic integration was used to estimate the transmission rate, importation, false negativity, and clearance separately for MRSA and VRE. The primary outcome was the intervention effect, which when less than (or greater than) zero, indicated a decreased (or increased) transmission rate attributable to the intervention. Results The transmission rate increased in both arms from pre- to postintervention (by 20% and 26% for MRSA and VRE). The estimated intervention effect was 0.00 (95% confidence interval [CI], −0.57 to 0.56) for MRSA and 0.05 (95% CI, −0.39 to 0.48) for VRE. Compared with MRSA, VRE had a higher transmission rate (preintervention, 0.0069 vs 0.0039; postintervention, 0.0087 vs 0.0046), higher importation probability (0.22 vs 0.17), and a lower clearance rate per colonized patient-day (0.016 vs 0.035). Conclusions Transmission rates in the 2 treatment arms were statistically indistinguishable from the pre- to postintervention phase, consistent with the original analysis of the STAR*ICU trial. Our statistical framework was able to disentangle transmission from importation and account for imperfect testing. Epidemiological differences between VRE and MRSA were revealed.

Key Points Critical roles for dynamic translational control during terminal erythroid differentiation. RBM38 can regulate translation during terminal erythropoiesis.

AbstractBackgroundSurvivors of childhood non‐Hodgkin lymphoma (NHL) are at increased risk for chronic health conditions. The objective of this study was to characterize health conditions, neurocognitive function, and physical performance among a clinically evaluated cohort of 200 childhood NHL survivors.MethodChronic health and neurocognitive conditions were graded as per a modified version of the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) and impaired physical function defined as performance < 10th percentile of normative data. Multivariable regression was used to investigate associations between sociodemographic characteristics, therapeutic exposures, and outcomes.ResultsSurvivors were a median age of 10 years (range 1–19) at diagnosis and 34 years (range 20–58) at evaluation. Eighty‐eight (44%) received radiation, 46 (23%) cranial radiation, and 69 (35%) high‐dose methotrexate. Most prevalent CTCAE Grades 3–4 (severe life‐threatening) conditions were obesity (35%), hypertension (9%), and impairment of executive function (13%), attention (9%), and memory (4%). Many had impaired strength (48%), flexibility (39%), muscular endurance (36%), and mobility (36%). Demographic and treatment‐related factors were associated with the development of individual chronic diseases and functional deficits.ConclusionsClinical evaluation identified a high prevalence of chronic health conditions, neurocognitive deficits, and performance limitations in childhood NHL survivors.

The purpose of this Letter to the Editor is to demonstrate an effective method for estimating viscoelasticity based on measurements of the Rayleigh surface wave speed. It is important to identify the surface wave mode for measuring surface wave speed. A concept of start frequency of surface waves is proposed. The surface wave speeds above the start frequency should be used to estimate the viscoelasticity of tissue. The motivation was to develop a noninvasive surface wave elastography (SWE) technique for assessing skin disease by measuring skin viscoelastic properties. Using an optical based SWE system, the author generated a local harmonic vibration on the surface of phantom using an electromechanical shaker and measured the resulting surface waves on the phantom using an optical vibrometer system. The surface wave speed was measured using a phase gradient method. It was shown that different standing wave modes were generated below the start frequency because of wave reflection. However, the pure symmetric surface waves were generated from the excitation above the start frequency. Using the wave speed dispersion above the start frequency, the viscoelasticity of the phantom can be correctly estimated.