Effects of unilateral vocal fold paralysis treatment with fascia augmentation on airflow mechanics, tracheal sounds and voice acoustics

Data on the influence of unilateral vocal fold paralysis on breathing, especially other than information obtained by spirometry, are relatively scarce. Even less is known about the effect of its treatment by vocal fold medialization. Consequently, there was a need to study the issue by combining multiple instruments capable of assessing airflow dynamics and voice. This need was emphasized by a recently developed medialization technique, autologous fascia injection; its effects on breathing have not previously been investigated. A cohort of ten patients with unilateral vocal fold paralysis was studied before and after autologous fascia injection by using flow-volume spirometry, body plethysmography and acoustic analysis of breathing and voice. Preoperative results were compared with those of ten healthy controls. A second cohort of 11 subjects with unilateral vocal fold paralysis was studied pre- and postoperatively by using flow-volume spirometry, impulse oscillometry, acoustic analysis of voice, voice handicap index and subjective assessment of dyspnoea. Preoperative peak inspiratory flow and specific airway conductance were significantly lower and airway resistance was significantly higher in the patients than in the healthy controls (78% vs. 107%, 73% vs. 116% and 182% vs. 125% of predicted; p = 0.004, p = 0.004 and p = 0.026, respectively). Patients had a higher root mean square of spectral power of tracheal sounds than controls, and three of them had wheezes as opposed to no wheezing in healthy subjects. Autologous fascia injection significantly improved acoustic parameters of the voice in both cohorts and voice handicap index in the latter cohort, indicating that this procedure successfully improved voice in unilateral vocal fold paralysis. Peak inspiratory flow decreased significantly as a consequence of this procedure (from 4.54 ± 1.68 l to 4.21 ± 1.26 l, p = 0.03, in pooled data of both cohorts), but no change occurred in the other variables of flow-volume spirometry, body-plethysmography and impulse oscillometry. Eight of the ten patients studied by acoustic analysis of breathing had wheezes after vocal fold medialization compared with only three patients before the procedure, and the numbers of wheezes per recorded inspirium and expirium increased significantly (from 0.02 to 0.42 and from 0.03 to 0.36; p = 0.028 and p = 0.043, respectively). In conclusion, unilateral vocal fold paralysis was observed to disturb forced breathing and also to cause some signs of disturbed tidal breathing. Findings of flow volume spirometry were consistent with variable extra-thoracic obstruction. Vocal fold medialization by autologous fascia injection improved the quality of the voice in patients with unilateral vocal fold paralysis, but also decreased peak inspiratory flow and induced wheezing during tidal breathing. However, these airflow changes did not appear to cause significant symptoms in patients.

Mechanics of Deformation under Traction and Friction of a Micrometric Monolithic MoS2 Particle in Comparison with those of an Agglomerate of Nanometric MoS2 Particles

Tribology of small inorganic nanoparticles in suspension in a liquid lubricant is often impaired because these particles agglomerate even when organic dispersants are used. In this paper we use lateral force microscopy to study the deformation mechanism and dissipation under traction of two extreme configurations (1) a large MoS2 particle (similar to 20 mu m width) of about 1 mu m height and (2) an agglomerate (similar to 20 mu m width), constituting 50 nm MoS2 crystallites, of about 1 mu m height. The agglomerate records a friction coefficient which is about 5-7 times that of monolithic particle. The paper examines the mechanisms of material removal for both the particles using continuum modeling and microscopy and infers that while the agglomerate response to traction can be accounted for by the bulk mechanical properties of the material, intralayer and interlayer basal planar slips determine the friction and wear of monolithic particles. The results provide a rationale for selection of layered particles, for suspension in liquid lubricants.

Asteroid identification using statistical orbital inversion methods

An efficient and statistically robust solution for the identification of asteroids among numerous sets of astrometry is presented. In particular, numerical methods have been developed for the short-term identification of asteroids at discovery, and for the long-term identification of scarcely observed asteroids over apparitions, a task which has been lacking a robust method until now. The methods are based on the solid foundation of statistical orbital inversion properly taking into account the observational uncertainties, which allows for the detection of practically all correct identifications. Through the use of dimensionality-reduction techniques and efficient data structures, the exact methods have a loglinear, that is, O(nlog(n)), computational complexity, where n is the number of included observation sets. The methods developed are thus suitable for future large-scale surveys which anticipate a substantial increase in the astrometric data rate. Due to the discontinuous nature of asteroid astrometry, separate sets of astrometry must be linked to a common asteroid from the very first discovery detections onwards. The reason for the discontinuity in the observed positions is the rotation of the observer with the Earth as well as the motion of the asteroid and the observer about the Sun. Therefore, the aim of identification is to find a set of orbital elements that reproduce the observed positions with residuals similar to the inevitable observational uncertainty. Unless the astrometric observation sets are linked, the corresponding asteroid is eventually lost as the uncertainty of the predicted positions grows too large to allow successful follow-up. Whereas the presented identification theory and the numerical comparison algorithm are generally applicable, that is, also in fields other than astronomy (e.g., in the identification of space debris), the numerical methods developed for asteroid identification can immediately be applied to all objects on heliocentric orbits with negligible effects due to non-gravitational forces in the time frame of the analysis. The methods developed have been successfully applied to various identification problems. Simulations have shown that the methods developed are able to find virtually all correct linkages despite challenges such as numerous scarce observation sets, astrometric uncertainty, numerous objects confined to a limited region on the celestial sphere, long linking intervals, and substantial parallaxes. Tens of previously unknown main-belt asteroids have been identified with the short-term method in a preliminary study to locate asteroids among numerous unidentified sets of single-night astrometry of moving objects, and scarce astrometry obtained nearly simultaneously with Earth-based and space-based telescopes has been successfully linked despite a substantial parallax. Using the long-term method, thousands of realistic 3-linkages typically spanning several apparitions have so far been found among designated observation sets each spanning less than 48 hours.

Process variability-aware statistical hybrid modeling of dynamic power dissipation in 65 nm CMOS designs

A generalized technique is proposed for modeling the effects of process variations on dynamic power by directly relating the variations in process parameters to variations in dynamic power of a digital circuit. The dynamic power of a 2-input NAND gate is characterized by mixed-mode simulations, to be used as a library element for 65mn gate length technology. The proposed methodology is demonstrated with a multiplier circuit built using the NAND gate library, by characterizing its dynamic power through Monte Carlo analysis. The statistical technique of Response. Surface Methodology (RSM) using Design of Experiments (DOE) and Least Squares Method (LSM), are employed to generate a "hybrid model" for gate power to account for simultaneous variations in multiple process parameters. We demonstrate that our hybrid model based statistical design approach results in considerable savings in the power budget of low power CMOS designs with an error of less than 1%, with significant reductions in uncertainty by atleast 6X on a normalized basis, against worst case design.

Author response: "Missed opportunity related to statistical methods"

"We thank MrGilder for his considered comments and suggestions for alternative analyses of our data. We also appreciate Mr Gilder’s support of our call for larger studies to contribute to the evidence base for preoperative loading with high-carbohydrate fluids..."

Two elementary proofs of the Wigner theorem on symmetry in quantum mechanics

In quantum theory, symmetry has to be defined necessarily in terms of the family of unit rays, the state space. The theorem of Wigner asserts that a symmetry so defined at the level of rays can always be lifted into a linear unitary or an antilinear antiunitary operator acting on the underlying Hilbert space. We present two proofs of this theorem which are both elementary and economical. Central to our proofs is the recognition that a given Wigner symmetry can, by post-multiplication by a unitary symmetry, be taken into either the identity or complex conjugation. Our analysis often focuses on the behaviour of certain two-dimensional subspaces of the Hilbert space under the action of a given Wigner symmetry, but the relevance of this behaviour to the larger picture of the whole Hilbert space is made transparent at every stage.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Ethylation of benzene: A statistical study

A central composite rotatable experimental design was constructed for a statistical study of the ethylation of benzene in the liquid phase, with aluminum chloride catalyst, in an agitated tank system. The conversion of benzene and ethylene and the yield of monoethyl- and diethylbenzene are characterized by the response surface technique. In the experimental range studied, agitation rate has no significant effect. Catalyst concentration, rate of ethylene Flow, and temperature are the influential factors. The response surfaces may be adequately approximated by planes.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Data fusion and matching by maximizing statistical dependencies

The core aim of machine learning is to make a computer program learn from the experience. Learning from data is usually defined as a task of learning regularities or patterns in data in order to extract useful information, or to learn the underlying concept. An important sub-field of machine learning is called multi-view learning where the task is to learn from multiple data sets or views describing the same underlying concept. A typical example of such scenario would be to study a biological concept using several biological measurements like gene expression, protein expression and metabolic profiles, or to classify web pages based on their content and the contents of their hyperlinks. In this thesis, novel problem formulations and methods for multi-view learning are presented. The contributions include a linear data fusion approach during exploratory data analysis, a new measure to evaluate different kinds of representations for textual data, and an extension of multi-view learning for novel scenarios where the correspondence of samples in the different views or data sets is not known in advance. In order to infer the one-to-one correspondence of samples between two views, a novel concept of multi-view matching is proposed. The matching algorithm is completely data-driven and is demonstrated in several applications such as matching of metabolites between humans and mice, and matching of sentences between documents in two languages.