Connection between partial pressure, volatility, and the Soret effect elucidated using simulations of non-ideal liquid mixtures

Building on recent simulation work, it is demonstrated using molecular-dynamics (MD) simulations of two-component liquid mixtures that the chemical contribution to the Soret effect in two-component non-ideal fluid mixtures arises due to differences in how the partial pressures of the components respond to temperature and density gradients. Further insight is obtained by reviewing the connection between activity and deviations from Raoult's law in the measurement of the vapor pressure of a liquid mixture. A new parameter gamma, defined in a manner similar to the activity coefficient, is used to characterize differences deviations from ``ideal'' behavior. It is then shown that the difference gamma2-gamma1 is predictive of the sign of the Soret coefficient and is correlated to its magnitude. We hence connect the Soret effect to the relative volatility of the components of a liquid mixture, with the more volatile component enriched in the low-density, high-temperature region, and the less volatile component enriched in the high-density, low-temperature region. Because gamma is closely connected to the activity coefficient, this suggests the possibility that measurement of partial vapor pressures might be used to indirectly determine the Soret coefficient. It is proposed that the insight obtained here is quite general and should be applicable to a wide range of materials systems. An attempt is made to understand how these results might apply to other materials systems including interstitials in solids and multicomponent solids with interdiffusion occurring via a vacancy mechanism.Comment: 21 pages, 7 Figures, to be submitted to J. Chem Physic

Spatial assessments in texture analysis: what the radiologist needs to know

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing

Calpains Mediate Integrin Attachment Complex Maintenance of Adult Muscle in Caenorhabditis elegans

Two components of integrin containing attachment complexes, UNC-97/PINCH and UNC-112/MIG-2/Kindlin-2, were recently identified as negative regulators of muscle protein degradation and as having decreased mRNA levels in response to spaceflight. Integrin complexes transmit force between the inside and outside of muscle cells and signal changes in muscle size in response to force and, perhaps, disuse. We therefore investigated the effects of acute decreases in expression of the genes encoding these multi-protein complexes. We find that in fully developed adult Caenorhabditis elegans muscle, RNAi against genes encoding core, and peripheral, members of these complexes induces protein degradation, myofibrillar and mitochondrial dystrophies, and a movement defect. Genetic disruption of Z-line– or M-line–specific complex members is sufficient to induce these defects. We confirmed that defects occur in temperature-sensitive mutants for two of the genes: unc-52, which encodes the extra-cellular ligand Perlecan, and unc-112, which encodes the intracellular component Kindlin-2. These results demonstrate that integrin containing attachment complexes, as a whole, are required for proper maintenance of adult muscle. These defects, and collapse of arrayed attachment complexes into ball like structures, are blocked when DIM-1 levels are reduced. Degradation is also blocked by RNAi or drugs targeting calpains, implying that disruption of integrin containing complexes results in calpain activation. In wild-type animals, either during development or in adults, RNAi against calpain genes results in integrin muscle attachment disruptions and consequent sub-cellular defects. These results demonstrate that calpains are required for proper assembly and maintenance of integrin attachment complexes. Taken together our data provide in vivo evidence that a calpain-based molecular repair mechanism exists for dealing with attachment complex disruption in adult muscle. Since C. elegans lacks satellite cells, this mechanism is intrinsic to the muscles and raises the question if such a mechanism also exists in higher metazoans

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria

Cancer cachexia impairs neural respiratory drive in hypoxia but not hypercapnia

Abstract Background Cancer cachexia is an insidious process characterized by muscle atrophy with associated motor deficits, including diaphragm weakness and respiratory insufficiency. Although neuropathology contributes to muscle wasting and motor deficits in many clinical disorders, neural involvement in cachexia‐linked respiratory insufficiency has not been explored. Methods We first used whole‐body plethysmography to assess ventilatory responses to hypoxic and hypercapnic chemoreflex activation in mice inoculated with the C26 colon adenocarcinoma cell line. Mice were exposed to a sequence of inspired gas mixtures consisting of (i) air, (ii) hypoxia (11% O2) with normocapnia, (iii) hypercapnia (7% CO2) with normoxia, and (iv) combined hypercapnia with hypoxia (i.e. maximal chemoreflex response). We also tested the respiratory neural network directly by recording inspiratory burst output from ligated phrenic nerves, thereby bypassing influences from changes in diaphragm muscle strength, respiratory mechanics, or compensation through recruitment of accessory motor pools. Results Cachectic mice demonstrated a significant attenuation of the hypoxic tidal volume (0.26mL±0.01mL vs 0.30mL±0.01mL; p0.05), breathing frequency (392±5bpm vs 408±5bpm; p>0.05) and phrenic nerve (93.1±8.8% vs 111.1±13.2%; p>0.05) responses were not affected. Further, the concurrent hypercapnia/hypoxia tidal volume (0.45±0.01mL vs 0.45±0.01mL; p>0.05), breathing frequency (395±7bpm vs 400±3bpm; p>0.05), and phrenic nerve (106.8±7.1% vs 147.5±38.8%; p>0.05) responses were not different between C26 cachectic and control mice. Conclusions Breathing deficits associated with cancer cachexia are specific to the hypoxic ventilatory response and, thus, reflect disruptions in the hypoxic chemoafferent neural network. Diagnostic techniques that detect decompensation and therapeutic approaches that support the failing hypoxic respiratory response may benefit patients at risk for cancer cachectic‐associated respiratory failure