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TGFβ–SMAD signaling exerts a contextual effect that suppresses malignant growth early in epithelial tumorigenesis but promotes metastasis at later stages. Longstanding challenges in resolving this functional dichotomy may uncover new strategies to treat advanced carcinomas. The Krüppel-like transcription factor, KLF10, is a pivotal effector of TGFβ/SMAD signaling that mediates antiproliferative effects of TGFβ. In this study, we show how KLF10 opposes the prometastatic effects of TGFβ by limiting its ability to induce epithelial-to-mesenchymal transition (EMT). KLF10 depletion accentuated induction of EMT as assessed by multiple metrics. KLF10 occupied GC-rich sequences in the promoter region of the EMT-promoting transcription factor SLUG/SNAI2, repressing its transcription by recruiting HDAC1 and licensing the removal of activating histone acetylation marks. In clinical specimens of lung adenocarcinoma, low KLF10 expression associated with decreased patient survival, consistent with a pivotal role for KLF10 in distinguishing the antiproliferative versus prometastatic functions of TGFβ. Our results establish that KLF10 functions to suppress TGFβ-induced EMT, establishing a molecular basis for the dichotomy of TGFβ function during tumor progression. Cancer Res; 77(9); 2387–400. ©2017 AACR.

Description of Supplementary Figures 1-3

Supplementary Figure S1 KLF10 is recruited to, and suppresses the expression of the FOSB gene. Supplementary Figure S2 ChIP analysis of KLF10 occupancy on the SNAI2 gene upon in control and TGFβ-stimulated Panc1 cells. Supplementary Figure S3 Heatmap for mRNA expression of CDH1, MMP7, CDH2 and MMP2 based on Cancer Cell Line Encyclopedia (CCLE) gene expression data in a panel of lung and pancreatic cancer cell lines.

Supplemental table S1 Primers for gene expression and ChIP analysis (5'-3' orientation)

The present work investigates the ability of oxygenated Butanol on performance and exhaust emission characteristics of a single-cylinder, four-stroke, water-cooled, common rail direct injection (CRDI) engine. Experiments were performed at constant engine speed (1500 rpm) and six different load conditions, varying from 5 to 30 Nm. Based on CRDI engine experimental data, an artificial intelligence (AI)-affiliated adaptive neuro-fuzzy inference system (ANFIS) model has been formulated for predicting the output parameters, namely brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOX), unburned hydrocarbon (UBHC) and carbon monoxide (CO) by considering the engine load and Butanol share in the blend as input parameters. With the increasing Butanol share in the Diesel–Butanol blend, the BTE and BSEC were significantly increased, and exhaust gas emissions, especially NOX and CO, were also reduced. The developed AI-based ANFIS model has the capacity of mapping the relationship between input–output parameters of the CRDI engine with good accuracies. In this study, the statistical performances obtained from ANFIS predicted model are (0.0000107–0.0000755) of mean square error, (0.000353–0.001533) of mean square relative error, (0.999722–0.999939) of correlation coefficient and (0.999444–0.999878) of absolute fraction of variance, which elevated the model capability to a higher stage under Diesel–Butanol strategies.

Gilgit-Baltistan region is a Shia-majority region, illegally controlled by Pakistan and enjoys semi-provincial status as a part of Pakistan Occupied Jammu Kashmir (PoJK). Sectarian violence...

Manan Dwivedi, Trump’s America: Diplomacy and Economic Policies. New Delhi: Ane Books Pvt Ltd., 2020, pp. 222, ₹843.00 (Hardcover). ISBN-13: 978-9389212617.

Upgradation of biomass derived furanic molecules viz. Furfural and 5-hydroxymethyl furfural (HMF) through transfer hydrogenation using cyclohexanol as a hydrogen donor agent over CuMgO-based catalyst is reported here. CuMgO (2–10 wt% Cu loading) based catalysts were synthesized through hydrothermal route and thoroughly investigated by XRD, SEM, TEM, ICP-AES, BET-surface area, XPS, H2-TPR and DFT calculation. 5 wt% CuMgO-5 (designated as CuMgO-5) catalyst exhibited very high catalytic activity (>99% conversion of reactant) in the transfer hydrogenation of furanic molecules to corresponding alcohols with ∼100% selectivity at 180 °C after 7 h of reaction. Catalytic experiments and detailed characterization of the catalyst indicate that high Cu dispersion and the presence of metallic Cu on the MgO surface are key parameters for its high catalytic activity. Furthermore, the kinetics studies showed that the reaction is sensitive with reaction temperature. DFT calculation reveals the endergonic process of H2 removal from cyclohexanol and the exergonic process of H2 addition to carbonyl group of furanic molecules.

Stem cells establish cortical polarity and divide asymmetrically to simultaneously maintain themselves and generate differentiating offspring cells. Several chromatin modifiers have been identified as stemness factors in mammalian pluripotent stem cells, but whether these factors control stem cell polarity and asymmetric division has not been investigated so far. We addressed this question in Drosophila neural stem cells called neuroblasts. We identified the Tip60 chromatin remodeling complex and its interaction partner Myc as regulators of genes required for neuroblast maintenance. Knockdown of Tip60 complex members results in loss of cortical polarity, symmetric neuroblast division, and premature differentiation through nuclear entry of the transcription factor Prospero. We found that aPKC is the key target gene of Myc and the Tip60 complex subunit Domino in regulating neuroblast polarity. Our transcriptome analysis further showed that Domino regulates the expression of mitotic spindle genes previously identified as direct Myc targets. Our findings reveal an evolutionarily conserved functional link between Myc, the Tip60 complex, and the molecular network controlling cell polarity and asymmetric cell division.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a particularly dismal prognosis. Histone deacetylases (HDAC) are epigenetic modulators whose activity is frequently deregulated in various cancers including PDAC. In particular, class-I HDACs (HDAC 1, 2, 3 and 8) have been shown to play an important role in PDAC. In this study, we investigated the effects of the class I-specific HDAC inhibitor (HDACi) 4SC-202 in multiple PDAC cell lines in promoting tumor cell differentiation. We show that 4SC-202 negatively affects TGFβ signaling and inhibits TGFβ-induced epithelial-to-mesenchymal transition (EMT). Moreover, 4SC-202 markedly induced p21 (CDKN1A) expression and significantly attenuated cell proliferation. Mechanistically, genome-wide studies revealed that 4SC-202-induced genes were enriched for Bromodomain-containing Protein-4 (BRD4) and MYC occupancy. BRD4, a well-characterized acetyllysine reader, has been shown to play a major role in regulating transcription of selected subsets of genes. Importantly, BRD4 and MYC are essential for the expression of a subgroup of genes induced by class-I HDACi. Taken together, our study uncovers a previously unknown role of BRD4 and MYC in eliciting the HDACi-mediated induction of a subset of genes and provides molecular insight into the mechanisms of HDACi action in PDAC.

We look at the design of an horizontal-axis wind turbine (HAWT) and the building of a model. The wind turbine is gear-less with three blades. Theory behind the construction and design of the blades is presented. The construction and assembly of the device is described. Finally, results are presented and then discussed. The average Power output performance for the blade NACA4412 comes out to be 6W which is slightly higher than that of NACA0012 which is 4.75W.

Globalisation is the most defining feature of the twenty first century. The globalised world faces new challenges such as climate change, terrorism, poverty and economic crisis. These challenges having no borders and cannot be solved by any country alone. These can be resolved only by coordinated efforts at the global level. Therefore, the world needs more effective forms of collaboration between international organisations, governments, the private sector and global civil society. The paper is an attempt to elucidate the role of global civil society as a means of global governance for the resolution of many global issues. More particularly, the aim is to understand how global civil society organisations are shaping the processes of global governance, and what their implications might be for the quality of governance at the international level.

Digital business is causing an upheaval that is shaking the foundations of traditional business. More and more companies recognize the opportunity the internet offers and start to establish an online presence with a sound business model behind it. Increased revenues and additional customers who return voluntarily to the company are drivers that bring more and more companies to digitize their offerings. Through the Internet it is possible to invent new and innovative ways to add value to existing products and services without necessarily spending a lot of money. Electronic business is a super set of business cases, which have been digitized and work now on the Internet. An e-business category is defined by the business case and not by the technology used to implement it. Over time more and more type of business will be converted to a digital form, even though it may seem impossible today. Technology is moving fast to make things possible tomorrow, which seemed impossible today.

We present two efficient discrete parameter simulation optimization (DPSO) algorithms for the long-run average cost objective. One of these algorithms uses the smoothed functional approximation (SFA) procedure, while the other is based on simultaneous perturbation stochastic approximation (SPSA). The use of SFA for DPSO had not been proposed previously in the literature. Further, both algorithms adopt an interesting technique of random projections that we present here for the first time. We give a proof of convergence of our algorithms. Next, we present detailed numerical experiments on a problem of admission control with dependent service times. We consider two different settings involving parameter sets that have moderate and large sizes, respectively. On the first setting, we also show performance comparisons with the well-studied optimal computing budget allocation (OCBA) algorithm and also the equal allocation algorithm. Note to Practitioners-Even though SPSA and SFA have been devised in the literature for continuous optimization problems, our results indicate that they can be powerful techniques even when they are adapted to discrete optimization settings. OCBA is widely recognized as one of the most powerful methods for discrete optimization when the parameter sets are of small or moderate size. On a setting involving a parameter set of size 100, we observe that when the computing budget is small, both SPSA and OCBA show similar performance and are better in comparison to SFA, however, as the computing budget is increased, SPSA and SFA show better performance than OCBA. Both our algorithms also show good performance when the parameter set has a size of 10(8). SFA is seen to show the best overall performance. Unlike most other DPSO algorithms in the literature, an advantage with our algorithms is that they are easily implementable regardless of the size of the parameter sets and show good performance in both scenarios.

We consider a stochastic differential equation (SDE) model of slotted Aloha with the retransmission probability as the associated parameter. We formulate the problem in both (a) the finite horizon and (b) the infinite horizon average cost settings. We apply the algorithm of 3] for the first setting, while for the second, we adapt a related algorithm from 2] that was originally developed in the simulation optimization framework. In the first setting, we obtain an optimal parameter trajectory that prescribes the parameter to use at any given instant while in the second setting, we obtain an optimal time-invariant parameter. Our algorithms are seen to exhibit good performance.

We develop four algorithms for simulation-based optimization under multiple inequality constraints. Both the cost and the constraint functions are considered to be long-run averages of certain state-dependent single-stage functions. We pose the problem in the simulation optimization framework by using the Lagrange multiplier method. Two of our algorithms estimate only the gradient of the Lagrangian, while the other two estimate both the gradient and the Hessian of it. In the process, we also develop various new estimators for the gradient and Hessian. All our algorithms use two simulations each. Two of these algorithms are based on the smoothed functional (SF) technique, while the other two are based on the simultaneous perturbation stochastic approximation (SPSA) method. We prove the convergence of our algorithms and show numerical experiments on a setting involving an open Jackson network. The Newton-based SF algorithm is seen to show the best overall performance.

In this paper, the design of compensated cascaded integrator comb filter (CIC) has been presented using maximally flat (MF) error minimization method for addressing the problem of pass-band droop. The coefficients of the second order and fourth order compensation filters are obtained by applying Maximally Flat conditions. The proposed sixth order CIC compensation filter is the result of cascade of second order and fourth order compensation filters. The proposed compensation filter reduces the pass-band droop of CIC by considerable amount. The comparison has been done with that of the compensation done by the fourth order compensation filter.

In this paper, the design of compensated generalized comb filter (GCF) is presented using maximally flat (MF) minimization technique. The proposed filter’s coefficients are obtained by MF technique. The proposed GCF compensation filter is the cascade of two second order compensation filter with GCF. Simulation result demonstrates that the passband droop of GCF is reduces to a large extent with the proposed filter.

We have examined the stability of triangular equilibrium points in photogravitational elliptic restricted three-body problem with Poynting-Robertson drag. We suppose that smaller primary is an oblate spheroid. We have taken bigger primary as radiating. We have found the location of triangular equilibrium points and characteristic equation of the problem. We conclude that triangular equilibrium points remain unstable, different from classical case.

We consider the problem of estimating the optimal parameter trajectory over a finite time interval in a parameterized stochastic differential equation (SDE), and propose a simulation-based algorithm for this purpose. Towards this end, we consider a discretization of the SDE over finite time instants and reformulate the problem as one of finding an optimal parameter at each of these instants. A stochastic approximation algorithm based on the smoothed functional technique is adapted to this setting for finding the optimal parameter trajectory. A proof of convergence of the algorithm is presented and results of numerical experiments over two different settings are shown. The algorithm is seen to exhibit good performance. We also present extensions of our framework to the case of finding optimal parameterized feedback policies for controlled SDE and present numerical results in this scenario as well.

We propose certain discrete parameter variants of well known simulation optimization algorithms. Two of these algorithms are based on the smoothed functional (SF) technique while two others are based on the simultaneous perturbation stochastic approximation (SPSA) method. They differ from each other in the way perturbations are obtained and also the manner in which projections and parameter updates are performed. All our algorithms use two simulations and two-timescale stochastic approximation. As an application setting, we consider the important problem of admission control of packets in communication networks under dependent service times. We consider a discrete time slotted queueing model of the system and consider two different scenarios - one where the service times have a dependence on the system state and the other where they depend on the number of arrivals in a time slot. Under our settings, the simulated objective function appears ill-behaved with multiple local minima and a unique global minimum characterized by a sharp dip in the objective function in a small region of the parameter space. We compare the performance of our algorithms on these settings and observe that the two SF algorithms show the best results overall. In fact, in many cases studied, SF algorithms converge to the global minimum.

In this paper, we have performed first order normalization in the photogravitational elliptic restricted three body problem with Poynting-Robertson drag. We suppose that bigger primary as radiating and smaller primary is an oblate spheroid. We have found the Lagrangian and the Hamiltonian of the problem. Then, we have expanded the Lagrangian function in power series of x and y, where (x, y) are the coordinates of the triangular equilibrium points. Using Whittaker (1965) method, we have found that the second order part H2 of the Hamiltonian is transformed into the normal form.