AsTAR: Sustainable Energy Harvesting for the Internet of Things through Adaptive Task Scheduling

Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.

Expect: EXplainable Prediction Model for Energy ConsumpTion

With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.

Pathophysiological Integration of Metabolic Reprogramming in Breast Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer.

Performance Evaluation of Thermal Bridge Reduction Method for Balcony in Apartment Buildings

Most apartment buildings in South Korea use internal insulation systems to reduce building energy demand. However, thermal bridges such as balcony slabs in apartment buildings still lead to significant heat loss in winter, because the internal insulation system is not continuous in the balcony slab structure, and floor heating systems are commonly used in residential buildings. Therefore, this study investigates two types of thermal break elements, namely thermal break (TB) and thermal break-fiber glass reinforced polymer (TB-GFRP), to improve the thermal resistance of a balcony thermal bridge. To understand the effects of balcony thermal bridges with and without thermal break elements, the linear thermal transmittances of different balcony thermal bridges were analyzed using Physibel simulations. Then, the heating demand of a model apartment under varying thermal bridge conditions was evaluated using TRNSYS simulations. To understand the effect of insulation systems on heat loss through a balcony thermal bridge, apartments with internal and external insulation systems were studied. Whether the apartment was heating was also considered in the thermal transmittance analysis. Thus, the linear thermal transmittance of the thermal bridges with thermal break elements was reduced by more than 60%, and the heating energy demands were reduced by more than 8%.

Glucose sparing by glycogenolysis (GSG) determines the relationship between brain metabolism and neurotransmission

Over the last two decades, it has been established that glucose metabolic fluxes in neurons and astrocytes are proportional to the rates of the glutamate/GABA-glutamine neurotransmitter cycles in close to 1:1 stoichiometries across a wide range of functional energy demands. However, there is presently no mechanistic explanation for these relationships. We present here a theoretical meta-analysis that tests whether the brain’s unique compartmentation of glycogen metabolism in the astrocyte and the requirement for neuronal glucose homeostasis lead to the observed stoichiometries. We found that blood-brain barrier glucose transport can be limiting during activation and that the energy demand could only be met if glycogenolysis supports neuronal glucose metabolism by replacing the glucose consumed by astrocytes, a mechanism we call Glucose Sparing by Glycogenolysis (GSG). The predictions of the GSG model are in excellent agreement with a wide range of experimental results from rats, mice, tree shrews, and humans, which were previously unexplained. Glycogenolysis and glucose sparing dictate the energy available to support neuronal activity, thus playing a fundamental role in brain function in health and disease.

Muscle-to-Brain Signaling Via Myokines and Myometabolites

Skeletal muscle health and function are important determinants of systemic metabolic homeostasis and organism-wide responses, including disease outcome. While it is well known that exercise protects the central nervous system (CNS) from aging and disease, only recently this has been found to depend on the endocrine capacity of skeletal muscle. Here, we review muscle-secreted growth factors and cytokines (myokines), metabolites (myometabolites), and other unconventional signals (e.g. bioactive lipid species, enzymes, and exosomes) that mediate muscle-brain and muscle-retina communication and neuroprotection in response to exercise and associated processes, such as the muscle unfolded protein response and metabolic stress. In addition to impacting proteostasis, neurogenesis, and cognitive functions, muscle-brain signaling influences complex brain-dependent behaviors, such as depression, sleeping patterns, and biosynthesis of neurotransmitters. Moreover, myokine signaling adapts feeding behavior to meet the energy demands of skeletal muscle. Contrary to protective myokines induced by exercise and associated signaling pathways, inactivity and muscle wasting may derange myokine expression and secretion and in turn compromise CNS function. We propose that tailoring muscle-to-CNS signaling by modulating myokines and myometabolites may combat age-related neurodegeneration and brain diseases that are influenced by systemic signals.

Lipid Metabolism in Cancer: The Role of Acylglycerolphosphate Acyltransferases (AGPATs)

Altered lipid metabolism is an emerging hallmark of aggressive tumors, as rapidly proliferating cancer cells reprogram fatty acid (FA) uptake, synthesis, storage, and usage to meet their increased energy demands. Central to these adaptive changes, is the conversion of excess FA to neutral triacylglycerides (TAG) and their storage in lipid droplets (LDs). Acylglycerolphosphate acyltransferases (AGPATs), also known as lysophosphatidic acid acyltransferases (LPAATs), are a family of five enzymes that catalyze the conversion of lysophosphatidic acid (LPA) to phosphatidic acid (PA), the second step of the TAG biosynthesis pathway. PA, apart from its role as an intermediate in TAG synthesis, is also a precursor of glycerophospholipids and a cell signaling molecule. Although the different AGPAT isoforms catalyze the same reaction, they appear to have unique non-overlapping roles possibly determined by their distinct tissue expression and substrate specificity. This is best exemplified by the role of AGPAT2 in the development of type 1 congenital generalized lipodystrophy (CGL) and is also manifested by recent studies highlighting the involvement of AGPATs in the physiology and pathology of various tissues and organs. Importantly, AGPAT isoform expression has been shown to enhance proliferation and chemoresistance of cancer cells and correlates with increased risk of tumor development or aggressive phenotypes of several types of tumors.

A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging

Mitochondria, key organelles which keep in tune with energy demands for eukaryotic cells, are firmly associated with neurological conditions and post-traumatic rehabilitation. In vivo fluorescence imaging of mitochondria, especially with...