Thermal conductivity of comets

A value is described for the thermal conductivity of the frost layer and for the water-ice solid debris mixture. The value of the porous structure is discussed as a function of depth only. Graphs show thermal conductivity as a function of depth and temperature at constant porosity and density

A Note on He’s Parameter-Expansion Method of Coupled Van der Pol–Duffing Oscillators

This paper presents the analytical and approximate solutions of the coupled chaotic Van der Pol-Duffing systems, by using the He\u27s parameter-expansion method (PEM). One iteration is sufficient to obtain a highly accurate solution, which is valid for the whole solution domain. From the obtained results, we can conclude that the suggest method, is of utter simplicity, and can be easily extended to all kinds of non-linear equations

Numerical Simulation for Solving Fractional Riccati and Logistic Differential Equations as a Difference Equation

In this paper, we introduce a numerical treatment using the generalized Euler method (GEM) for the fractional (Caputo sense) Riccati and Logistic differential equations. In the proposed method, we invert the given model as a difference equation. We compare our numerical solutions with the exact solution and with those numerical solutions using the fourth-order Runge-Kutta method (RK4). The obtained numerical results of the two proposed problem models show the simplicity and efficiency of the proposed method

Facile one-pot synthesis of CuO nanospheres: Sensitive electrochemical determination of hydrazine in water effluents

Hydrazine (HZ) is massively used in several industrial applications. Adsorption of HZ through human skin creates carcinogenicity by disturbing the human organ system and thus, the quantification of HZ levels in environmental water samples is highly needed. The present work describes the short-term development of copper oxide nanospheres (CuO NS) by one-step wet chemical approach and their implementation on glassy carbon electrode (GCE) for the sensitive and selective quantification of the environmentally hazardous HZ. The CuO NS formation was identified by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM) and UV-visible spectroscopy. SEM images exhibited the uniform CuO NS with an average size of 85 nm. The linker-free CuO NS modified GCE offered high electrocatalytic activity against HZ determination by showing the linear range determination in the range of 0.5 to 500 µM, with the detection limit of 63 nM (S/N=3), and sensitivity of 894.28 µA mM-1 cm-2. Further, the developed HZ sensor displayed excellent repeatability and reproducibility and was successfully exploited for the determination of HZ in real environmental samples, implying that GCE/CuO-NS is a confident and low-cost electrochemical platform for HZ determination

The current state of animal models and genomic approaches towards identifying and validating molecular determinants of Mycobacterium tuberculosis infection and tuberculosis disease

Animal models are important in understanding both the pathogenesis of and immunity to tuberculosis (TB). Unfortunately, we are beginning to understand that no animal model perfectly recapitulates the human TB syndrome, which encompasses numerous different stages. Furthermore, Mycobacterium tuberculosis infection is a very heterogeneous event at both the levels of pathogenesis and immunity. This review seeks to establish the current understanding of TB pathogenesis and immunity, as validated in the animal models of TB in active use today. We especially focus on the use of modern genomic approaches in these models to determine the mechanism and the role of specific molecular pathways. Animal models have significantly enhanced our understanding of TB. Incorporation of contemporary technologies such as single cell transcriptomics, high-parameter flow cytometric immune profiling, proteomics, proteomic flow cytometry and immunocytometry into the animal models in use will further enhance our understanding of TB and facilitate the development of treatment and vaccination strategies

Antiretroviral therapy timing impacts latent tuberculosis infection reactivation in a Mycobacterium tuberculosis/SIV coinfection model

Studies using the nonhuman primate model of Mycobacterium tuberculosis/simian immunodeficiency virus coinfection have revealed protective CD4+ T cell-independent immune responses that suppress latent tuberculosis infection (LTBI) reactivation. In particular, chronic immune activation rather than the mere depletion of CD4+ T cells correlates with reactivation due to SIV coinfection. Here, we administered combinatorial antiretroviral therapy (cART) 2 weeks after SIV coinfection to study whether restoration of CD4+ T cell immunity occurred more broadly, and whether this prevented reactivation of LTBI compared to cART initiated 4 weeks after SIV. Earlier initiation of cART enhanced survival, led to better control of viral replication, and reduced immune activation in the periphery and lung vasculature, thereby reducing the rate of SIV-induced reactivation. We observed robust CD8+ T effector memory responses and significantly reduced macrophage turnover in the lung tissue. However, skewed CD4+ T effector memory responses persisted and new TB lesions formed after SIV coinfection. Thus, reactivation of LTBI is governed by very early events of SIV infection. Timing of cART is critical in mitigating chronic immune activation. The potential novelty of these findings mainly relates to the development of a robust animal model of human M. tuberculosis/HIV coinfection that allows the testing of underlying mechanisms

Numerical Studies for Solving Fractional Riccati Differential Equation

In this paper, finite difference method (FDM) and Pade\u27-variational iteration method (Pade\u27- VIM) are successfully implemented for solving the nonlinear fractional Riccati differential equation. The fractional derivative is described in the Caputo sense. The existence and the uniqueness of the proposed problem are given. The resulting nonlinear system of algebraic equations from FDM is solved by using Newton iteration method; moreover the condition of convergence is verified. The convergence\u27s domain of the solution is improved and enlarged by Pade\u27-VIM technique. The results obtained by using FDM is compared with Pade\u27-VIM. It should be noted that the Pade\u27-VIM is preferable because it always converges to the solution even for large domain

Primary healthcare reform in the United Nations Relief and Works Agency for Palestine Refugees in the Near East.

Palestinian refugees served by the United Nation Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) are experiencing increasing rates of diagnosis of non-communicable diseases. In response, in 2011 UNRWA initiated an Agency-wide programme of primary healthcare reform, informed by the Chronic Care Model framework. Health services were reorganized following a family-centred approach, with delivery by multidisciplinary family health teams supported by updated technical advice. An inclusive clinical information system, termed e-Health, was implemented to collect a wide range of health information, with a focus on continuity of treatment. UNRWA was able to bring about these wide-ranging changes within its existing resources, reallocating finances, reforming its payment mechanisms, and modernizing its drug-procurement policies. While specific components of UNRWA's primary healthcare reform are showing promising results, additional efforts are needed to empower patients further and to strengthen involvement of the community

Differentiating Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features from Classic Papillary Thyroid Carcinoma: Analysis of Cytomorphologic Descriptions Using a Novel Machine-Learning Approach.

Background:Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples. Methods:Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs). Results:SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance, P \u3c 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC. Conclusions:This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis

Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach

3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neurodegenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping. We have developed a SVM-based classifier to predict domain swapping events using a set of features derived from sequence and structural data. The SVM classifier was trained on features derived from 150 proteins reported to be involved in 3D domain swapping and 150 proteins not known to be involved in swapped conformation or related to proteins involved in swapping phenomenon. The testing was performed using 63 proteins from the positive dataset and 63 proteins from the negative dataset. We obtained 76.33% accuracy from training and 73.81% accuracy from testing. Due to high diversity in the sequence, structure and functions of proteins involved in domain swapping, availability of such an algorithm to predict swapping events from sequence and structure-derived features will be an initial step towards identification of more putative proteins that may be involved in swapping or proteins involved in deposition disease. Further, the top features emerging in our feature selection method may be analysed further to understand their roles in the mechanism of domain swapping