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Environmental drug resistance constitutes a serious impediment for therapeutic intervention in multiple myeloma. Tumor-promoting cytokines, such as tumor necrosis factor (TNF), induce nuclear factor-κB (NFκB)- driven expression of pro-survival factors, which confer resistance in myeloma cells to apoptotic insults from TNF-related apoptosis-inducing ligand (TRAIL) and other chemotherapeutic drugs. It is thought that RelA:p50 dimer, activated from IκBα-inhibited complex in response to TNF-induced canonical NFκB signal, mediates the pro-survival NFκB function in cancerous cells. Myeloma cells additionally acquire gain-of-function mutations in the non-canonical NFκB module, which induces partial proteolysis of p100 into p52 to promote RelB:p52/NFκB activation from p100-inhibited complex during immune cell differentiation. However, role of non-canonical NFκB signaling in the drug resistance in multiple myeloma remains unclear. Here we report that myeloma-associated non-canonical aberrations reinforce pro-survival TNF signaling in producing a protracted TRAIL-refractory state. These mutations did not act through a typical p52 NFκB complex, but completely degraded p100 to reposition RelB under IκBα control, whose degradation during TNF signaling induced an early RelB:p50 containing NFκB activity. More so, autoregulatory RelB synthesis prolonged this TNF-induced RelB:p50 activity in myeloma cells harboring non-canonical mutations. Intriguingly, TNF-activated RelB:p50 dimer was both necessary and sufficient, and RelA was not required, for NFκB-dependent pro-survival gene expressions and suppression of apoptosis. Indeed, high RelB mRNA expressions in myeloma patients correlated with the augmented level of pro-survival factors and resistance to therapeutic intervention. In sum, we provide evidence that cancer-associated mutations perpetuate TNF-induced pro-survival NFκB activity through autoregulatory RelB control and thereby exacerbate environmental drug resistance in multiple myeloma.

Environmental drug resistance constitutes a serious impediment for therapeutic intervention in multiple myeloma. Tumor-promoting cytokines, such as tumor necrosis factor (TNF), induce nuclear factor-[kappa]B (NF[kappa]B)- driven expression of pro-survival factors, which confer resistance in myeloma cells to apoptotic insults from TNF-related apoptosis-inducing ligand (TRAIL) and other chemotherapeutic drugs. It is thought that RelA:p50 dimer, activated from I[kappa]B[alpha]-inhibited complex in response to TNF-induced canonical NF[kappa]B signal, mediates the pro-survival NF[kappa]B function in cancerous cells. Myeloma cells additionally acquire gain-of-function mutations in the non-canonical NF[kappa]B module, which induces partial proteolysis of p100 into p52 to promote RelB:p52/NF[kappa]B activation from p100-inhibited complex during immune cell differentiation. However, role of non-canonical NF[kappa]B signaling in the drug resistance in multiple myeloma remains unclear. Here we report that myeloma-associated non-canonical aberrations reinforce pro-survival TNF signaling in producing a protracted TRAIL-refractory state. These mutations did not act through a typical p52 NF[kappa]B complex, but completely degraded p100 to reposition RelB under I[kappa]B[alpha] control, whose degradation during TNF signaling induced an early RelB:p50 containing NF[kappa]B activity. More so, autoregulatory RelB synthesis prolonged this TNF-induced RelB:p50 activity in myeloma cells harboring non-canonical mutations. Intriguingly, TNF-activated RelB:p50 dimer was both necessary and sufficient, and RelA was not required, for NF[kappa]B-dependent pro-survival gene expressions and suppression of apoptosis. Indeed, high RelB mRNA expressions in myeloma patients correlated with the augmented level of pro-survival factors and resistance to therapeutic intervention. In sum, we provide evidence that cancer-associated mutations perpetuate TNF-induced pro-survival NF[kappa]B activity through autoregulatory RelB control and thereby exacerbate environmental drug resistance in multiple myeloma.

This paper presents a novel study on soil image classification, leveraging the synergistic potential of transfer learning and convolutional neural networks (CNNs). The proposed approach combines the strengths of the MobileNetV2 architecture with a customized CNN model for accurate and efficient soil type recognition. The pre-trained MobileNetV2 is used to capture generic features before fine-tuning it with a dedicated soil image dataset comprising four distinct classes: red, clay, black, and yellow soils. To enhance the model’s capacity for discerning intricate soil textures, a specially designed CNN architecture is incorporated. The model’s performance is rigorously evaluated on a dataset of 108 images, each sized at 256 × 256 pixels, achieving an exceptional accuracy rate of 100% on the test dataset. The promising results demonstrate the efficacy of the proposed methodology in soil image classification tasks, offering potential applications in precision agriculture, environmental monitoring, and land management. While these findings showcase remarkable accuracy, further investigations are recommended to assess the model’s generalization across diverse environmental conditions and an expanded range of soil image datasets.

Internet of Things isn’t always approximately about the things themselves; it’s approximately being clever. IoT is real and helpful because of its ability to apply intelligence to sense facts, especially in the context of construction engineering. A smart building’s architecture is a great place to start as IoT is reshaping every aspect of a building, from design to occupancy to maintenance. The experience of workers, control, and tenants is being optimized through the use of IoT data to inform decision-making. Better facilities may simplify corporate processes and increase revenue in smarter homes. The goal of intelligent houses is sustainability. There are several methods for automating tasks with the Internet of Things. It is necessary to address every single facet of the building architecture. This article discusses the problems and technologies of IoT-based smart building architecture. The Internet of Things (IoT) and embedded systems provide the foundation of the “Smart Building” idea. Together with smart lighting in smart buildings and seismic detection, the model that is being shown has several features. When the smart lighting system turns on and off, it is determined by the amount of natural light available and the presence of people within the building. In order to reduce the amount of maintenance needed, smart dustbins that open up when they sense a person are available. Watering systems that are designed to measure the moisture content of the soil are extremely useful for the maintenance of lawns. A seismic activity detection module allows for early warnings of earthquakes and other seismic activity that may occur in the future. It has been successfully developed a smart building concept that uses Arduino and a cloud server to analyze the data gathered from the smart building.

Cyclic convolution is also known as circular convolution. It is simpler to compute and produces less output samples compared to linear convolution. Given x(n)and h(n), the length - N cyclic convolution can be expressed as where (n-K) mod N returns the remainder of the integer division of n-k by N. There are many architectures for calculating cyclinc convolution of any two signals. Implementation using Fermat Number Transform (FNT) is one of them. Fermat Number is a positive integer of the form Fn = 22n +1 where n is a nonnegative integer. The basic property of FN is that they are recursive. This paper presents a high speed parallel architecture for cyclic convolution based on Fermat Number Transform (FNT) in the diminished-1 number system. A code conversion method without addition (CCWA) and a butterfly operation method without addition (BOWA) are proposed to perform the FNT and its inverse (IFNT) except their final stages in the convolution. The point wise multiplication in the convolution is accomplished by modulo 2n+1 partial product multipliers (MPPM) and output partial products which are inputs to the IFNT. Thus modulo 2n+1 carry propagation additions are avoided in the FNT and the IFNT except their final stages and the modulo 2n+1 multiplier. The execution delay of the parallel architecture is reduced evidently to the decrease of modulo 2n +1 carry-propagation addition. General Terms This paper studying parallel architecture design for various applications in VLSI Design, for high speed data transformation, using Fermat number transform, different designing method to provide high speed data transmission.

Bivariate count models having one marginal and the other conditionals being of the Poissons form are called pseudo-Poisson distributions. Such models have simple exible dependence structures, possess fast computation algorithms and generate a sufficiently large number of parametric families. It has been strongly argued that the pseudo-Poisson model will be the first choice to consider in modelling bivariate over-dispersed data with positive correlation and having one of the marginal equi-dispersed. Yet, before we start fitting, it is necessary to test whether the given data is compatible with the assumed pseudo-Poisson model. Hence, in the present note we derive and propose a few goodness-of-fit tests for the bivariate pseudo-Poisson distribution. Also we emphasize two tests, a lesser known test based on the supremes of the absolute difference between the estimated probability generating function and its empirical counterpart. A new test has been proposed based on the difference between the estimated bivariate Fisher dispersion index and its empirical indices. However, we also consider the potential of applying the bivariate tests that depend on the generating function (like the Kocherlakota and Kocherlakota and Mu~noz and Gamero tests) and the univariate goodness-of-fit tests (like the Chi-square test) to the pseudo-Poisson data. However, for each of the tests considered we analyse finite, large and asymptotic properties. Nevertheless, we compare the power (bivariate classical Poisson and Com-Max bivariate Poisson as alternatives) of each of the tests suggested and also include examples of application to real-life data. In a nutshell we are developing an R package which includes a test for compatibility of the data with the bivariate pseudo-Poisson model.