This paper studies multiuser wireless powered communication networks, where energy constrained users charge their energy storages by scavenging energy of the radio frequency signals radiated from a ...hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users in both infinite capacity and finite capacity energy storage cases. First, when the users are equipped with the infinite capacity energy storages, we derive the optimal downlink energy transmission policy at the H-AP. Based on this result, analytical resource allocation solutions are obtained. Next, we propose the optimal energy and time allocation algorithm for the case where each user has finite capacity energy storage. Simulation results confirm that the proposed algorithms offer about 30% average sum rate performance gain over conventional schemes.
This paper considers multiple-input single-output simultaneous wireless information and power transfer (SWIPT) broadcast channels (BCs) where a multi-antenna transmitter serves single antenna ...receivers each equipped with a time switching (TS) circuit for information decoding (ID) and energy harvesting (EH). To be specific, we study a scheme which jointly determines the time durations allocated for the ID and the EH modes at each receiver and the transmit covariance matrices at the transmitter. Then, we present a general joint TS protocol for the SWIPT BC which includes conventional TS schemes as special cases. In order to fully characterize the performance of the proposed joint TS systems, the achievable rate region is analyzed under EH constraint at the receivers. By applying the rate profile methods, we identify the optimal TS ratios and the optimal transmit covariance matrices which achieve the boundary points of the rate region. Then, the boundary points are obtained by solving the average transmit power minimization problems with individual rate constraints at the receivers. To solve these non-convex problems, the original problems are decoupled into subproblems with fixed auxiliary variables. Then, the globally optimal TS ratios and the transmit covariance matrices are computed by finding the optimal auxiliary variables via convex optimization techniques. Numerical results demonstrate that the proposed joint TS scheme outperforms conventional TS methods.
Autophagy is a highly conserved self-digestion process, which is essential for maintaining homeostasis and viability in response to nutrient starvation. Although the components of autophagy in the ...cytoplasm have been well studied, the molecular basis for the transcriptional and epigenetic regulation of autophagy is poorly understood. Here we identify co-activator-associated arginine methyltransferase 1 (CARM1) as a crucial component of autophagy in mammals. Notably, CARM1 stability is regulated by the SKP2-containing SCF (SKP1-cullin1-F-box protein) E3 ubiquitin ligase in the nucleus, but not in the cytoplasm, under nutrient-rich conditions. Furthermore, we show that nutrient starvation results in AMP-activated protein kinase (AMPK)-dependent phosphorylation of FOXO3a in the nucleus, which in turn transcriptionally represses SKP2. This repression leads to increased levels of CARM1 protein and subsequent increases in histone H3 Arg17 dimethylation. Genome-wide analyses reveal that CARM1 exerts transcriptional co-activator function on autophagy-related and lysosomal genes through transcription factor EB (TFEB). Our findings demonstrate that CARM1-dependent histone arginine methylation is a crucial nuclear event in autophagy, and identify a new signalling axis of AMPK-SKP2-CARM1 in the regulation of autophagy induction after nutrient starvation.
Obesity is characterized by an accumulation of macrophages in adipose, some of which form distinct crown-like structures (CLS) around fat cells. While multiple discrete adipose tissue macrophage ...(ATM) subsets are thought to exist, their respective effects on adipose tissue, and the transcriptional mechanisms that underlie the functional differences between ATM subsets, are not well understood. We report that obese fat tissue of mice and humans contain multiple distinct populations of ATMs with unique tissue distributions, transcriptomes, chromatin landscapes, and functions. Mouse Ly6c ATMs reside outside of CLS and are adipogenic, while CD9 ATMs reside within CLS, are lipid-laden, and are proinflammatory. Adoptive transfer of Ly6c ATMs into lean mice activates gene programs typical of normal adipocyte physiology. By contrast, adoptive transfer of CD9 ATMs drives gene expression that is characteristic of obesity. Importantly, human adipose tissue contains similar ATM populations, including lipid-laden CD9 ATMs that increase with body mass. These results provide a higher resolution of the cellular and functional heterogeneity within ATMs and provide a framework within which to develop new immune-directed therapies for the treatment of obesity and related sequela.
Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic ...regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.
Expression of a few master transcription factors can reprogram the epigenetic landscape and three-dimensional chromatin topology of differentiated cells and achieve pluripotency. During ...reprogramming, thousands of long-range chromatin contacts are altered, and changes in promoter association with enhancers dramatically influence transcription. Molecular participants at these sites have been identified, but how this re-organization might be orchestrated is not known. Biomolecular condensation is implicated in subcellular organization, including the recruitment of RNA polymerase in transcriptional activation. Here, we show that reprogramming factor KLF4 undergoes biomolecular condensation even in the absence of its intrinsically disordered region. Liquid-liquid condensation of the isolated KLF4 DNA binding domain with a DNA fragment from the NANOG proximal promoter is enhanced by CpG methylation of a KLF4 cognate binding site. We propose KLF4-mediated condensation as one mechanism for selectively organizing and re-organizing the genome based on the local sequence and epigenetic state.
Multimodel combining approaches can extract reliable climate information from a large number of climate projections by exploiting the strengths and discounting the weaknesses of each climate ...simulator; however, most of them (e.g., reliability ensemble averaging REA) assign weights to climate simulators without accounting for spatial and temporal variabilities in climate model skills. Here we tested several REAs and proposed a full version that reflects the spatiotemporal (ST) variability of climate model skills. Interperformance evaluations between REA versions showed that, on average, ST‐REA reduced the bias by 33.78% and the root mean square error by 11.61%. Therefore, spatial and temporal variabilities in climate model skills can enhance the overall reliability of precipitation projections. ST‐REA was applied to project future precipitations over South Korea by combining seven climate models: The spatially averaged projected changes were 2.77%, 8.15%, and 7.58% for the 2020–2039, 2040–2069, and 2070–2099 periods, respectively.
Plain Language Summary
The spatiotemporal variability of climate characteristics is one of the most pronounced climate behaviors. Thus, considering spatiotemporal variabilities during the assessment of climate models’ skills when performing a multimodel averaging may improve the overall reliability of the multimodel ensemble estimate. Nevertheless, most of the available multimodel averaging approaches (e.g., the REA) do not consider in parallel the spatial and temporal variabilities during climate model weighting. In this study, we presented an improved version of the REA, which can characterize the spatiotemporal variabilities at multiple sites and time steps during multimodel averaging. The proposed REA versions have improved weight assignation mechanisms (in respect to each climate simulator) and can handle multiple weather stations.
Key Points
Three augmented versions of reliability ensemble averaging were proposed to achieve reliable precipitation projections over South Korea
The spatiotemporal variability of climate model skills within a multimodel approach improved the overall reliability of precipitation projections
The spatiotemporal reliability ensemble averaging outperformed all versions of reliability ensemble averaging in precipitation projection
Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is ...derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.
Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support ...vector machines (MSVMs) have become a very appealing machine-learning approach due to their good performance. Until now, researchers have proposed a variety of techniques for adapting support vector machines (SVMs) to multi-class classification, since SVMs were originally devised for binary classification. However, most of them have only focused on classifying samples into nominal categories; thus, the unique characteristic of credit-rating – ordinality – seldom has been considered in the proposed approaches. This study proposes a new type of MSVM classifier (named OMSVM) that is designed to extend the binary SVMs by applying an ordinal pairwise partitioning (OPP) strategy. Our model can efficiently and effectively handle multiple ordinal classes. To validate OMSVM, we applied it to a real-world case of bond rating. We compared the results of our model with those of conventional MSVM approaches and other AI techniques including MDA, MLOGIT, CBR, and ANNs. The results showed that our proposed model improves the performance of classification in comparison to other typical multi-class classification techniques and uses fewer computational resources.
Examining enzyme kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the ...enzyme kinetic parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the kinetic parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme kinetics is provided.
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