Raoult Was Right After All.

Raoult's law, published in 1887, is taught in chemistry and chemical engineering fields as a first approximation to the vapor pressure and activity of solutes and solvents in mixtures. In ideal solutions, it is exact but many solutions are known to have substantial deviations from Raoult's law as conventionally interpreted. In 1908, Callendar showed that water hydrated to the solute can explain some of the departures from Raoult's law in aqueous solution. Here, we show that by simply assuming equilibria between the free water in solution and its hydrated forms, Raoult's law and Callendar's extension are valid over the full range of concentrations, while also showing how water and solutes interact in solution. This model of solutions has importance in highly concentrated solutions common in atmosphere aerosols relevant to climate change and air quality, and in numerous industrial processes

Detecting Alterations in Pulmonary Airway Development with Airway-by-Airway Comparison

Neonatal and postnatal exposures to air pollutants have adverse effects on lung development resulting in airway structure changes. Usually, generation-averaged analysis of airway geometric parameters is employed to differentiate between pulmonary airway trees. However, this method is limited, especially for monopodial branching trees such as in rat airways, because both quite proximal and less proximal airways that have very different structure and function may be in the same generation. To avoid limitations inherent in generation averaging, we developed a method that compares two trees airway-by-airway using micro CT image data from rat lungs. This computerized technique (1) identifies the geometry and architecture of the conducting airways from CT images, (2) extracts the main tree, (3) associates paired airways from the two different trees, and (4) develops summary statistics on the degree of similarity between populations of animals. By comparing the trees airway-by-airway, we found that the variance in airway length of the group exposed to diffusion flame particles (DFP) is significantly larger than the group raised in filtered air (FA). This method also found that rotation angle of the DFP group is significantly larger than FA, which is not as certain in the generation-based analysis. We suggest that airway-by-airway analysis complements generation-based averaging for detecting airway alterations

Effect of voicing and articulation manner on aerosol particle emission during human speech.

Previously, we demonstrated a strong correlation between the amplitude of human speech and the emission rate of micron-scale expiratory aerosol particles, which are believed to play a role in respiratory disease transmission. To further those findings, here we systematically investigate the effect of different 'phones' (the basic sound units of speech) on the emission of particles from the human respiratory tract during speech. We measured the respiratory particle emission rates of 56 healthy human volunteers voicing specific phones, both in isolation and in the context of a standard spoken text. We found that certain phones are associated with significantly higher particle production; for example, the vowel /i/ ("need," "sea") produces more particles than /ɑ/ ("saw," "hot") or /u/ ("blue," "mood"), while disyllabic words including voiced plosive consonants (e.g., /d/, /b/, /g/) yield more particles than words with voiceless fricatives (e.g., /s/, /h/, /f/). These trends for discrete phones and words were corroborated by the time-resolved particle emission rates as volunteers read aloud from a standard text passage that incorporates a broad range of the phones present in spoken English. Our measurements showed that particle emission rates were positively correlated with the vowel content of a phrase; conversely, particle emission decreased during phrases with a high fraction of voiceless fricatives. Our particle emission data is broadly consistent with prior measurements of the egressive airflow rate associated with the vocalization of various phones that differ in voicing and articulation. These results suggest that airborne transmission of respiratory pathogens via speech aerosol particles could be modulated by specific phonetic characteristics of the language spoken by a given human population, along with other, more frequently considered epidemiological variables

Direct surface analysis of time-resolved aerosol impactor samples with ultrahigh-resolution mass spectrometry.

Aerosol particles in the atmosphere strongly influence the Earth's climate and human health, but the quantification of their effects is highly uncertain. The complex and variable composition of atmospheric particles is a main reason for this uncertainty. About half of the particle mass is organic material, which is very poorly characterized on a molecular level, and therefore it is challenging to identify sources and atmospheric transformation processes. We present here a new combination of techniques for highly time-resolved aerosol sampling using a rotating drum impactor (RDI) and organic chemical analysis using direct liquid extraction surface analysis (LESA) combined with ultrahigh-resolution mass spectrometry. This minimizes sample preparation time and potential artifacts during sample workup compared to conventional off-line filter or impactor sampling. Due to the high time resolution of about 2.5 h intensity correlations of compounds detected in the high-resolution mass spectra were used to identify groups of compounds with likely common sources or atmospheric history.Financial support by the UK Natural Environment Research Council (NERC), project NE/H52449X/1 and by Advion Bioscience LTD via a CASE Award is greatly acknowledged.This is the accepted manuscript version. The final version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ac3020615

A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example

Large air quality models and large climate models simulate the physical and chemical properties of the ocean, land surface, and/or atmosphere to predict atmospheric composition, energy balance and the future of our planet. All of these models employ some form of operator splitting, also called the method of fractional steps, in their structure, which enables each physical or chemical process to be simulated in a separate operator or module within the overall model. In this structure, each of the modules calculates property changes for a fixed period of time; that is, property values are passed into the module, which calculates how they change for a period of time and then returns the new property values, all in round-robin between the various modules of the model. Some of these modules require the vast majority of the computer resources consumed by the entire model, so increasing their computational efficiency can either improve the model's computational performance, enable more realistic physical or chemical representations in the module, or a combination of these two. Recent efforts have attempted to replace these modules with ones that use machine learning tools to memorize the input–output relationships of the most time-consuming modules. One shortcoming of some of the original modules and their machine-learned replacements is lack of adherence to conservation principles that are essential to model performance. In this work, we derive a mathematical framework for machine-learned replacements that conserves properties – say mass, atoms, or energy – to machine precision. This framework can be used to develop machine-learned operator replacements in environmental models.TU Berlin, Open-Access-Mittel – 202

Predicting muscle forces of individuals with hemiparesis following stroke

<p>Abstract</p> <p>Background</p> <p>Functional electrical stimulation (FES) has been used to improve function in individuals with hemiparesis following stroke. An ideal functional electrical stimulation (FES) system needs an accurate mathematical model capable of designing subject and task-specific stimulation patterns. Such a model was previously developed in our laboratory and shown to predict the isometric forces produced by the quadriceps femoris muscles of able-bodied individuals and individuals with spinal cord injury in response to a wide range of clinically relevant stimulation frequencies and patterns. The aim of this study was to test our isometric muscle force model on the quadriceps femoris, ankle dorsiflexor, and ankle plantar-flexor muscles of individuals with post-stroke hemiparesis.</p> <p>Methods</p> <p>Subjects were seated on a force dynamometer and isometric forces were measured in response to a range of stimulation frequencies (10 to 80-Hz) and 3 different patterns. Subject-specific model parameter values were obtained by fitting the measured force responses from 2 stimulation trains. The model parameters thus obtained were then used to obtain predicted forces for a range of frequencies and patterns. Predicted and measured forces were compared using intra-class correlation coefficients, r²values, and model error relative to the physiological error (variability of measured forces).</p> <p>Results</p> <p>Results showed excellent agreement between measured and predicted force-time responses (r²>0.80), peak forces (ICCs>0.84), and force-time integrals (ICCs>0.82) for the quadriceps, dorsiflexor, and plantar-fexor muscles. The <it>model error </it>was within or below the +95% confidence interval of the <it>physiological error </it>for >88% comparisons between measured and predicted forces.</p> <p>Conclusion</p> <p>Our results show that the model has potential to be incorporated as a feed-forward controller for predicting subject-specific stimulation patterns during FES.</p

Effects of early life exposure to traffic-related air pollution on brain development in juvenile Sprague-Dawley rats

Epidemiological studies link traffic-related air pollution (TRAP) to increased risk for various neurodevelopmental disorders (NDDs); however, there are limited preclinical data demonstrating a causal relationship between TRAP and adverse neurodevelopmental outcomes. Moreover, much of the preclinical literature reports effects of concentrated ambient particles or diesel exhaust that do not recapitulate the complexity of real-world TRAP exposures. To assess the developmental neurotoxicity of more realistic TRAP exposures, we exposed male and female rats during gestation and early postnatal development to TRAP drawn directly from a traffic tunnel in Northern California and delivered to animals in real-time. We compared NDD-relevant neuropathological outcomes at postnatal days 51-55 in TRAP-exposed animals versus control subjects exposed to filtered air. As indicated by immunohistochemical analyses, TRAP significantly increased microglial infiltration in the CA1 hippocampus, but decreased astrogliosis in the dentate gyrus. TRAP exposure had no persistent effect on pro-inflammatory cytokine levels in the male or female brain, but did significantly elevate the anti-inflammatory cytokine IL-10 in females. In male rats, TRAP significantly increased hippocampal neurogenesis, while in females, TRAP increased granule cell layer width. TRAP had no effect on apoptosis in either sex. Magnetic resonance imaging revealed that TRAP-exposed females, but not males, also exhibited decreased lateral ventricular volume, which was correlated with increased granule cell layer width in the hippocampus in females. Collectively, these data indicate that exposure to real-world levels of TRAP during gestation and early postnatal development modulate neurodevelopment, corroborating epidemiological evidence of an association between TRAP exposure and increased risk of NDDs