Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of neural network model as an excellent computational tool in nanofluid field

Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies

The entropy per rapidity $d S/d y$ produced in central Pb-Pb ultra-relativistic nuclear collisions at LHC energies is calculated using experimentally observed identified particle spectra and source radii estimated from Hanbury Brown-Twiss (HBT) for particles, $\pi$, $k$, $p$, $\Lambda$, $\Omega$, and $\bar{\Sigma}$, and $\pi$, $k$, $p$, $\Lambda$ and $K_s^0$ at $\sqrt{s}$ $=2.76$ and $5.02$ TeV, respectively. Artificial neural network (ANN) simulation model is used to estimate the entropy per rapidity $d S/d y$ at the considered energies. The simulation results are compared with equivalent experimental data, and good agreement is achieved. A mathematical equation describes experimental data is obtained. Extrapolating the transverse momentum spectra at $p_T$ $=0$ is required to calculate $d S/d y$ thus we use two different fitting functions, Tsallis distribution and the Hadron Resonance Gas (HRG) model. The success of ANN model to describe the experimental measurements will imply further prediction for the entropy per rapidity in the absence of the experiment

FOXA1 repression is associated with loss of BRCA1 and increased promoter methylation and chromatin silencing in breast cancer

FOXA1 expression correlates with the breast cancer luminal subtype and patient survival. RNA and protein analysis of a panel of breast cancer cell lines revealed that BRCA1 deficiency is associated with the downregulation of FOXA1 expression. Knockdown of BRCA1 resulted in the downregulation of FOXA1 expression and enhancement of FOXA1 promoter methylation in MCF-7 breast cancer cells, whereas the reconstitution of BRCA1 in Brca1-deficent mouse mammary epithelial cells (MMECs) promoted Foxa1 expression and methylation. These data suggest that BRCA1 suppresses FOXA1 hypermethylation and silencing. Consistently, the treatment of MMECs with the DNA methylation inhibitor 5-aza-2'-deoxycitydine induced Foxa1 mRNA expression. Furthermore, treatment with GSK126, an inhibitor of EZH2 methyltransferase activity, induced FOXA1 expression in BRCA1-deficient but not in BRCA1-reconstituted MMECs. Likewise, the depletion of EZH2 by small interfering RNA enhanced FOXA1 mRNA expression. Chromatin immunoprecipitation (ChIP) analysis demonstrated that BRCA1, EZH2, DNA methyltransferases (DNMT)1/3a/3b and H3K27me3 are recruited to the endogenous FOXA1 promoter, further supporting the hypothesis that these proteins interact to modulate FOXA1 methylation and repression. Further co-immunoprecipitation and ChIP analysis showed that both BRCA1 and DNMT3b form complexes with EZH2 but not with each other, consistent with the notion that BRCA1 binds to EZH2 and negatively regulates its methyltransferase activity. We also found that EZH2 promotes and BRCA1 impairs the deposit of the gene silencing histone mark H3K27me3 on the FOXA1 promoter. These associations were validated in a familial breast cancer patient cohort. Integrated analysis of the global gene methylation and expression profiles of a set of 33 familial breast tumours revealed that FOXA1 promoter methylation is inversely correlated with the transcriptional expression of FOXA1 and that BRCA1 mutation breast cancer is significantly associated with FOXA1 methylation and downregulation of FOXA1 expression, providing physiological evidence to our findings that FOXA1 expression is regulated by methylation and chromatin silencing and that BRCA1 maintains FOXA1 expression through suppressing FOXA1 gene methylation in breast cancer.Oncogene advance online publication, 22 December 2014; doi:10.1038/onc.2014.421.published_or_final_versio

Adapting to time: why nature evolved a diverse set of neurons

Brains have evolved a diverse set of neurons with varying morphologies, physiological properties and rich dynamics that impact their processing of temporal information. By contrast, most neural network models include a homogeneous set of units that only vary in terms of their spatial parameters (weights and biases). To investigate the importance of temporal parameters to neural function, we trained spiking neural networks on tasks of varying temporal complexity, with different subsets of parameters held constant. We find that in a tightly resource constrained setting, adapting conduction delays is essential to solve all test conditions, and indeed that it is possible to solve these tasks using only temporal parameters (delays and time constants) with weights held constant. In the most complex spatio-temporal task we studied, we found that an adaptable bursting parameter was essential. More generally, allowing for adaptation of both temporal and spatial parameters increases network robustness to noise, an important feature for both biological brains and neuromorphic computing systems. In summary, our findings highlight how rich and adaptable dynamics are key to solving temporally structured tasks at a low neural resource cost, which may be part of the reason why biological neurons vary so dramatically in their physiological properties.Comment: 14 pages, 6 figure

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3 and reports on seven research projects.Joint Services Electronics Program Contract DAAL03-89-C-0001National Science Foundation Contract ECS 86-20029Schlumberger- Doll ResearchU.S. Army Research Office Contract DAAL03 88-K-0057National Aeronautics and Space Administration Contract NAGW-1617U.S. Navy - Office of Naval Research Contract N00014-89-J-1107National Aeronautics and Space Administration Contract NAGW-1272National Aeronautics and Space Administration Contract 958461Simulation Technologies Contract DAAH01-87-C-0679U.S. Army Corp of Engineers Contract DACA39-87-K-0022WaveTracer, Inc.U.S. Navy - Office of Naval Research Contract N00014-89-J-1019U.S. Air Force Systems - Electronic Systems Division Contract F19628-88-K-0013Digital Equipment CorporationInternational Business Machines CorporationU.S. Department of Transportation Contract DTRS-57-88-C-0007

High dose intravenous immunoglobulin in Rh and ABO hemolytic disease of Egyptian neonates

Background: Despite advances made in the use of phototherapy, and in order to avoid sequelae of kernicterus, the treatment of hyperbilirubinemia may require one or several exchange transfusions, an invasive therapy which is not without risk. Intravenous immune globulin treatment in isoimmune hyperbilirubinemia has been shown to be effective, but the response to treatment is variable. Objective: To evaluate effectiveness of high dose Intravenous immune globulin (HD-IVIG) in reducing the need for exchange transfusion, duration of phototherapy and/or hospitalization in neonates with isoimmune hemolytic disease due to Rh or ABO incompatibility. Methods: The study included 116 direct Coombs' test positive neonates delivered at Gynecology and Obstetrics Hospital of Ain Shams University, Cairo, Egypt. They were randomly assigned to receive phototherapy with HD-IVIG in a single dose of 1 gm/kg (60 neonates, intervention group) or phototherapy (56 neonates, control group). Results: Nine neonates in the intervention group (15%) and 23 (41%) in the control group required single exchange transfusion (p< 0.001). Multiple exchange transfusion was indicated in 15 neonates (26.8%) in the control group versus none in the intervention group (p< 0.001). Compared with control group, neonates in the intervention group had shorter mean duration of intensive phototherapy (9.97 versus 35.5 hours, p<0.001) and hospital stay (27.9 versus 103.5 hours, p< 0.001). No adverse effects of HD-IVIG administration were noted. Conclusion: HD-IVIG effectively reduced the requirement for exchange transfusion and duration of phototherapy and hospitalization in isoimmune hemolytic disease of the newborn.Keywords: Hemolytic disease of newborn; hyperbilirubinemia; exchange transfusion; high dose intravenous immunoglobulin

Math meets the Clinic: Modeling Patient Specific HNSCC Radiation Response Dynamics

Head and neck squamous cell carcinomas (HNSCCs) originate from the mucosal lining of the upper aerodigestive tract and radiation therapy has become a fundamental component in the standard care for these patients. However, the treatment\u27s unique dynamics can lead to disparities in outcomes and biological responses in both tumor and normal tissues. A significant challenge remains in predicting individual patient responses to radiation, with variability often resulting in under or over-treatment and potentially adverse effects. The absence of reliable biomarkers highlights the need for predictive measures to guide clinical decisions in real-time. The integration of mathematical models in radiation therapy offers a promising solution, with models incorporating a global lambda demonstrating the ability to predict treatment responses beyond initial weeks. These models provide a framework to address patient response variability, potentially improving survival rates and quality of life.https://openworks.mdanderson.org/radonc24/1003/thumbnail.jp

MYC functions are specific in biological subtypes of breast cancer and confers resistance to endocrine therapy in luminal tumours.

BACKGROUND: MYC is amplified in approximately 15% of breast cancers (BCs) and is associated with poor outcome. c-MYC protein is multi-faceted and participates in many aspects of cellular function and is linked with therapeutic response in BCs. We hypothesised that the functional role of c-MYC differs between molecular subtypes of BCs. METHODS: We therefore investigated the correlation between c-MYC protein expression and other proteins involved in different cellular functions together with clinicopathological parameters, patients' outcome and treatments in a large early-stage molecularly characterised series of primary invasive BCs (n=1106) using immunohistochemistry. The METABRIC BC cohort (n=1980) was evaluated for MYC mRNA expression and a systems biology approach utilised to identify genes associated with MYC in the different BC molecular subtypes. RESULTS: High MYC and c-MYC expression was significantly associated with poor prognostic factors, including grade and basal-like BCs. In luminal A tumours, c-MYC was associated with ATM (P=0.005), Cyclin B1 (P=0.002), PIK3CA (P=0.009) and Ki67 (P<0.001). In contrast, in basal-like tumours, c-MYC showed positive association with Cyclin E (P=0.003) and p16 (P=0.042) expression only. c-MYC was an independent predictor of a shorter distant metastases-free survival in luminal A LN+ tumours treated with endocrine therapy (ET; P=0.013). In luminal tumours treated with ET, MYC mRNA expression was associated with BC-specific survival (P=0.001). In ER-positive tumours, MYC was associated with expression of translational genes while in ER-negative tumours it was associated with upregulation of glucose metabolism genes. CONCLUSIONS: c-MYC function is associated with specific molecular subtypes of BCs and its overexpression confers resistance to ET. The diverse mechanisms of c-MYC function in the different molecular classes of BCs warrants further investigation particularly as potential therapeutic targets

MindSet: Vision. A toolbox for testing DNNs on key psychological experiments

Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox MindSet: Vision, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-of-distribution classification, and decoder method), accessible at https://github.com/MindSetVision/mindset-vision. We test ResNet-152 on each of these methods as an example of how the toolbox can be used