The present work numerically analyses the heat transfer and entropy generation characteristics in a two-dimensional porous right-angled triangular enclosure with undulations on the left wall. The ...enclosure is heated sinusoidally from the bottom wall, while the left wall is maintained at a uniform temperature lower than the bottom one and the right inclined wall is adiabatic. The stream function-vorticity formulation with a finite difference scheme has been incorporated to simulate the results. The effects of Rayleigh number, Darcy number and undulations on the left wall on the heat transfer, fluid flow and entropy generation characteristics have been investigated. It has been revealed that for lower values of Rayleigh number, heat transfer is dominated by the conduction mechanism. However, for higher values of Rayleigh number, heat transfer by convection becomes significant. It has been further revealed that for higher values of Rayleigh number and Darcy number, the dominant source of entropy generation is due to fluid friction. Moreover, the entropy generation due to fluid friction is significantly higher in case of undulations on the wall as compared to the cases with no-undulation, whereas the entropy generation due to heat transfer is almost same for both the cases.
•Natural convection within a wavy triangular enclosure saturated with porous media is important in many engineering applications.•The stream function-vorticity equations are solved using finite difference method.•Effect of Rayleigh number and Darcy number on the fluid flow, heat transfer and entropy generation is examined.•Undulations on the left wall increases both heat transfer rate and the entropy generation in the enclosure.
ATP-dependent chromatin remodellers modulate nucleosome dynamics by mobilizing or disassembling nucleosomes, as well as altering nucleosome composition. These chromatin remodellers generally function ...by translocating along nucleosomal DNA at the H3-H4 interface of nucleosomes. Here we show that, unlike other remodellers, INO80 translocates along DNA at the H2A-H2B interface of nucleosomes and persistently displaces DNA from the surface of H2A-H2B. DNA translocation and DNA torsional strain created near the entry site of nucleosomes by INO80 promotes both the mobilization of nucleosomes and the selective exchange of H2A.Z-H2B dimers out of nucleosomes and replacement by H2A-H2B dimers without any additional histone chaperones. We find that INO80 translocates and mobilizes H2A.Z-containing nucleosomes more efficiently than those containing H2A, partially accounting for the preference of INO80 to replace H2A.Z with H2A. Our data suggest that INO80 has a mechanism for dimer exchange that is distinct from other chromatin remodellers including its paralogue SWR1.
There exists an ongoing need to improve the validity and accuracy of computational fluid dynamics (CFD) simulations of turbulent airflows in the extra-thoracic and upper airways. Yet, a knowledge gap ...remains in providing experimentally-resolved 3D flow benchmarks with sufficient data density and completeness for useful comparison with widely-employed numerical schemes. Motivated by such shortcomings, the present work details to the best of our knowledge the first attempt to deliver in vitro-in silico correlations of 3D respiratory airflows in a generalized mouth-throat model and thereby assess the performance of Large Eddy Simulations (LES) and Reynolds-Averaged Numerical Simulations (RANS). Numerical predictions are compared against 3D volumetric flow measurements using Tomographic Particle Image Velocimetry (TPIV) at three steady inhalation flowrates varying from shallow to deep inhalation conditions. We find that a RANS k-ω SST model adequately predicts velocity flow patterns for Reynolds numbers spanning 1'500 to 7'000, supporting results in close proximity to a more computationally-expensive LES model. Yet, RANS significantly underestimates turbulent kinetic energy (TKE), thus underlining the advantages of LES as a higher-order turbulence modeling scheme. In an effort to bridge future endevours across respiratory research disciplines, we provide end users with the present in vitro-in silico correlation data for improved predictive CFD models towards inhalation therapy and therapeutic or toxic dosimetry endpoints.
Increasing fetal hemoglobin (HbF) levels in adult red blood cells provides clinical benefit to patients with sickle cell disease and some forms of β-thalassemia. To identify potentially druggable HbF ...regulators in adult human erythroid cells, we employed a protein kinase domain-focused CRISPR-Cas9-based genetic screen with a newly optimized single-guide RNA scaffold. The screen uncovered the heme-regulated inhibitor HRI (also known as EIF2AK1), an erythroid-specific kinase that controls protein translation, as an HbF repressor. HRI depletion markedly increased HbF production in a specific manner and reduced sickling in cultured erythroid cells. Diminished expression of the HbF repressor BCL11A accounted in large part for the effects of HRI depletion. Taken together, these results suggest HRI as a potential therapeutic target for hemoglobinopathies.
Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance ...when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.
Over the last 3 decades ATP-dependent chromatin remodelers have been thought to recognize chromatin at the level of single nucleosomes rather than higher-order organization of more than one ...nucleosome. We show the yeast ISW1a remodeler has such higher-order structural specificity, as manifested by large allosteric changes that activate the nucleosome remodeling and spacing activities of ISW1a when bound to dinucleosomes. Although the ATPase domain of Isw1 docks at the SHL2 position when ISW1a is bound to either mono- or di-nucleosomes, there are major differences in the interactions of the catalytic subunit Isw1 with the acidic pocket of nucleosomes and the accessory subunit Ioc3 with nucleosomal DNA. By mutational analysis and uncoupling of ISW1a's dinucleosome specificity, we find that dinucleosome recognition is required by ISW1a for proper chromatin organization at promoters; as well as transcription regulation in combination with the histone acetyltransferase NuA4 and histone H2A.Z exchanger SWR1.
Large amounts of net electrical charge are known to accumulate on inhaled aerosols during their generation using commonly-available inhalers. This effect often leads to superfluous deposition in the ...extra-thoracic airways at the cost of more efficient inhalation therapy. Since the electrostatic force is inversely proportional to the square of the distance between an aerosol and the airway wall, its role has long been recognized as potentially significant in the deep lungs. Yet, with the complexity of exploring such phenomenon directly at the acinar scales, in vitro experiments have been largely limited to upper airways models. Here, we devise a microfluidic alveolated airway channel coated with conductive material to quantify in vitro the significance of electrostatic effects on inhaled aerosol deposition. Specifically, our aerosol exposure assays showcase inhaled spherical particles of 0.2, 0.5, and 1.1 μm that are recognized to reach the acinar regions, whereby deposition is typically attributed to the leading roles of diffusion and sedimentation. In our experiments, electrostatic effects are observed to largely prevent aerosols from depositing inside alveolar cavities. Rather, deposition is overwhelmingly biased along the inter-alveolar septal spaces, even when aerosols are charged with only a few elementary charges. Our observations give new insight into the role of electrostatics at the acinar scales and emphasize how charged particles under 2 µm may rapidly overshadow the traditionally accepted dominance of diffusion or sedimentation when considering aerosol deposition phenomena in the deep lungs.
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Despite the prevalence of inhalation therapy in the treatment of various respiratory diseases, predicting and optimizing lung deposition fractions of inhaled drugs for maximal ...efficacy remains challenging due to the complex anatomical structures of the extra-thoracic airways, notably the glottal region. One of the widespread speculations in current insilico simulations lies in assuming a static glottis during inhalation, while in reality inhalation leads to significant glottis cross-sectional area expansion. The present work attempts to explore, insilico, the influence of transient movement of the glottal structures on inhalation therapy outcomes. To this end, we adopted a CT-based realistic human tracheobronchial tree (TB) model and explored transient airflows and deposition outcomes for a broad range of inhaled aerosols (i.e., dp=1-12 μm) under a dry powder inhaler (DPI) maneuver. Three different glottal expansion ratios, spanning static to 40 percent cross-sectional area expansion have been considered for the analysis. Our findings point to the tangible impact of glottal motion on airflow and particle deposition along the respiratory tract for a DPI maneuver, where a static glottis underpredicts the total particle deposition in the TB model for lower sized particles (dp≤ 3 μm) compared to predictions for all dynamic glottal motions. In contrast, for larger size particles (i.e., 5 ≤dp≤ 10 μm), a static glottis yields lower total deposition in the TB model compared with dynamic glottal motions. Our study also underlines that regional deposition of smaller micron-sized particles is most affected by glottal deformation in the conducting airways.
The present work demonstrates the effectiveness of the combination of time, frequency, time–frequency, and statistical features extracted from the electroencephalogram (EEG) data, with support vector ...machine (SVM) for lie detection. Predominantly, the features extracted from the empirical mode decomposition (EMD) of the EEG data significantly improve the classification accuracy. A specific number of narrow band oscillatory components, called intrinsic mode functions (IMFs), are obtained after EMD of the data. The first three IMFs are selected to extract three time and three frequency domain statistical features corresponding to each IMF. These features are chosen due to the strong data adaptation capability of EMD for the transient signals such as an EEG. Furthermore, the features are selected keeping in mind the differences in the distribution, average value, and regularity of the guilty and innocent subjects’ brain signals. The proposed combination of extracted features with customized SVM demonstrates better accuracy than the other state-of-the-art feature extraction methods reported earlier. The proposed hybrid combination of features prominently distinguishes the guilty and innocent subjects with the classification accuracy of 99.44%.
Benzodiazepines are the most commonly prescribed psychotropic medications, but they may place users at risk of serious adverse effects. Developing a method to predict benzodiazepine prescriptions ...could assist in prevention efforts.
The present study applies machine learning methods to de-identified electronic health record data, in order to develop algorithms for predicting benzodiazepine prescription receipt (yes/no) and number of benzodiazepine prescriptions (0, 1, 2+) at a given encounter. Support-vector machine (SVM) and random forest (RF) approaches were applied to outpatient psychiatry, family medicine, and geriatric medicine data from a large academic medical center. The training sample comprised encounters taking place between January 2020 and December 2021 (
= 204,723 encounters); the testing sample comprised data from encounters taking place between January and March 2022 (
= 28,631 encounters). The following empirically-supported features were evaluated: anxiety and sleep disorders (primary anxiety diagnosis, any anxiety diagnosis, primary sleep diagnosis, any sleep diagnosis), demographic characteristics (age, gender, race), medications (opioid prescription, number of opioid prescriptions, antidepressant prescription, antipsychotic prescription), other clinical variables (mood disorder, psychotic disorder, neurocognitive disorder, prescriber specialty), and insurance status (any insurance, type of insurance). We took a step-wise approach to developing a prediction model, wherein Model 1 included only anxiety and sleep diagnoses, and each subsequent model included an additional group of features.
For predicting benzodiazepine prescription receipt (yes/no), all models showed good to excellent overall accuracy and area under the receiver operating characteristic curve (AUC) for both SVM (Accuracy = 0.868-0.883; AUC = 0.864-0.924) and RF (Accuracy = 0.860-0.887; AUC = 0.877-0.953). Overall accuracy was also high for predicting number of benzodiazepine prescriptions (0, 1, 2+) for both SVM (Accuracy = 0.861-0.877) and RF (Accuracy = 0.846-0.878).
Results suggest SVM and RF algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received at a given encounter. If replicated, these predictive models could inform system-level interventions to reduce the public health burden of benzodiazepines.
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