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Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies help to better understand and model degradation and to optimize the operating strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications. To our knowledge, the dataset 1 presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to cells. The dataset 1 includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution). The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operating strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

Innate immune memory is a vital mechanism of myeloid cell plasticity that occurs in response to environmental stimuli and alters subsequent immune responses. Two types of immunological imprinting can be distinguished—training and tolerance. These are epigenetically mediated and enhance or suppress subsequent inflammation, respectively. Whether immune memory occurs in tissue-resident macrophages in vivo and how it may affect pathology remains largely unknown. Here we demonstrate that peripherally applied inflammatory stimuli induce acute immune training and tolerance in the brain and lead to differential epigenetic reprogramming of brain-resident macrophages (microglia) that persists for at least six months. Strikingly, in a mouse model of Alzheimer’s pathology, immune training exacerbates cerebral β -amyloidosis and immune tolerance alleviates it; similarly, peripheral immune stimulation modifies pathological features after stroke. Our results identify immune memory in the brain as an important modifier of neuropathology. Peripheral stimuli can induce acute immune training and tolerance in the brain and lead to long-lasting epigenetic reprogramming of microglia; these changes alter pathology in mouse models of stroke and Alzheimer’s pathology .

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

In recent years, development of age-related diseases, such as Alzheimer’s and Parkinson’s disease, as well as other brain disorders, including anxiety, depression, and schizophrenia have been shown to be associated with changes in the gut microbiome. Several factors can induce an alteration in the bacterial composition of the host`s gastrointestinal tract. Besides dietary changes and frequent use of antibiotics, the microbiome is also profoundly affected by aging. Levels of microbiota-derived metabolites are elevated in older individuals with age-associated diseases and cognitive defects compared to younger, healthy age groups. The identified metabolites with higher concentration in aged hosts, which include choline and trimethylamine, are known risk factors for age-related diseases. While the underlying mechanisms and pathways remain elusive for the most part, it has been shown, that these metabolites are able to trigger the innate immunity in the central nervous system by influencing development and activation status of brain-resident macrophages. The macrophages residing in the brain comprise parenchymal microglia and non-parenchymal macrophages located in the perivascular spaces, meninges, and the choroid plexus. In this review, we highlight the impact of age on the composition of the microbiome and microbiota-derived metabolites and their influence on age-associated diseases caused by dysfunctional brain-resident macrophages.

The immune system is crucial for defending against various invaders, such as pathogens, cancer cells or misfolded proteins. With increasing age, the diminishing immune response, known as immunosenescence, becomes evident. Concomitantly, some diseases like infections, autoimmune diseases, chronic inflammatory diseases and cancer, accumulate with age. Different cell types are part of the innate immunity response and produce soluble factors, cytokines, chemokines, and type I interferons. Improper maturation of innate immune cells or their dysfunction have been linked to numerous age-related diseases. In parallel to the occurrence of the many functional facets of the immune response, a symbiotic microbiota had been acquired. For the relevant and situation-dependent function of the immune system the microbiome plays an essential role because it fine-tunes the immune system and its responses during life. Nevertheless, how the age-related alterations in the microbiota are reflected in the innate immune system, is still poorly understood. With this review, we provide an up-to-date overview on our present understanding of the gut microbiota effects on innate immunity, with a particular emphasis on aging-associated changes in the gut microbiota and the implications for the brain innate immune response.

TGFβ1 has been implicated in regulating functional aspects of several distinct immune cell populations including central nervous system (CNS) resident microglia. Activation and priming of microglia have been demonstrated to contribute to the progression of neurodegenerative diseases and, thus, underlie stringent control by endogenous regulatory factors including TGFβ1. Here, we demonstrate that deletion of Tgfbr2 in adult postnatal microglia does neither result in impairment of the microglia-specific gene expression signatures, nor is microglial survival and maintenance affected. Tgfbr2 -deficient microglia were characterised by distinct morphological changes and transcriptome analysis using RNAseq revealed that loss of TGFβ signalling results in upregulation of microglia activation and priming markers. Moreover, protein arrays demonstrated increased secretion of CXCL10 and CCL2 accompanied by activation of immune cell signalling as evidenced by increased phosphorylation of TAK1. Together, these data underline the importance of microglial TGFβ signalling to regulate microglia adaptive changes. While previous studies had shown the requirement of TGFβ signalling in microglia gene expression, the specificity of the loss-of-function was unclear. Here, Zöller and colleagues generate microglia specific cKO of TGFβ receptor 2, and show dispensable function of Tgfbr2 in microglial survival and the requirement of Tgfbr2 in morphological and transcriptional homeostasis of adult microglia.

The widespread legalization of Cannabis has opened the industry to using contemporary analytical techniques for chemotype analysis. Chemotypic data has been collected on a large variety of oil profiles inherent to the cultivars that are commercially available. The unknown gene regulation and pharmacokinetics of dozens of cannabinoids offer opportunities of high interest in pharmacology research. Retailers in many medical and recreational jurisdictions are typically required to report chemical concentrations of at least some cannabinoids. Commercial cannabis laboratories have collected large chemotype datasets of diverse Cannabis cultivars. In this work a data set of 17,600 cultivars tested by Steep Hill Inc., is examined using machine learning techniques to interpolate missing chemotype observations and cluster cultivars into groups based on chemotype similarity. The results indicate cultivars cluster based on their chemotypes, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. Due to the missing data and to the low signal to noise ratio for some less common cannabinoids, their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.

Braf  V600E expression in resident macrophage progenitors leads to clonal expansion of ERK-activated microglia, which causes synaptic and neuronal loss in the brain and results in lethal neurodegenerative disease in adult mice. BRAF mutation begets brain disease Microglia—immune cells in the brain—derive from yolk-sac erythro-myeloid progenitors (EMPs), which are distinct from haematopoietic stem cells (HSCs). Frederic Geissmann and colleagues show that mosaic expression of a mutant BRAF, which activates the RAS–MEK–ERK pathway and causes tumours when expressed in HSCs, results in expansion of tissue-resident macrophages and late-onset neurodegeneration when expressed in EMPs. They show in a mouse model that neurobehavioural abnormalities, astrogliosis, deposition of amyloid precursor protein, synaptic loss and neuronal death are driven by ERK-activated microglia and can be prevented by BRAF inhibition. These results show that, in mice, activation of the MAP kinase pathway in microglia can cause neurodegeneration. These findings may explain the neurodegeneration observed in patients with histiocytosis—disorders of myeloid cell expansion associated with somatic mutations in the RAS–MEK–ERK pathway, such as the BRAF mutation studied here. The pathophysiology of neurodegenerative diseases is poorly understood and there are few therapeutic options. Neurodegenerative diseases are characterized by progressive neuronal dysfunction and loss, and chronic glial activation 1 . Whether microglial activation, which is generally viewed as a secondary process, is harmful or protective in neurodegeneration remains unclear 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 . Late-onset neurodegenerative disease observed in patients with histiocytoses 9 , 10 , 11 , 12 , which are clonal myeloid diseases associated with somatic mutations in the RAS–MEK–ERK pathway such as BRAF(V600E) 13 , 14 , 15 , 16 , 17 , suggests a possible role of somatic mutations in myeloid cells in neurodegeneration. Yet the expression of BRAF(V600E) in the haematopoietic stem cell lineage causes leukaemic and tumoural diseases but not neurodegenerative disease 18 , 19 . Microglia belong to a lineage of adult tissue-resident myeloid cells that develop during organogenesis from yolk-sac erythro-myeloid progenitors (EMPs) distinct from haematopoietic stem cells 20 , 21 , 22 , 23 . We therefore hypothesized that a somatic BRAF(V600E) mutation in the EMP lineage may cause neurodegeneration. Here we show that mosaic expression of BRAF(V600E) in mouse EMPs results in clonal expansion of tissue-resident macrophages and a severe late-onset neurodegenerative disorder. This is associated with accumulation of ERK-activated amoeboid microglia in mice, and is also observed in human patients with histiocytoses. In the mouse model, neurobehavioural signs, astrogliosis, deposition of amyloid precursor protein, synaptic loss and neuronal death were driven by ERK-activated microglia and were preventable by BRAF inhibition. These results identify the fetal precursors of tissue-resident macrophages as a potential cell-of-origin for histiocytoses and demonstrate that a somatic mutation in the EMP lineage in mice can drive late-onset neurodegeneration. Moreover, these data identify activation of the MAP kinase pathway in microglia as a cause of neurodegeneration and this offers opportunities for therapeutic intervention aimed at the prevention of neuronal death in neurodegenerative diseases.

Understanding and optimizing industrial energy systems requires datasets that capture detailed electrical behavior at high temporal resolution over long time periods. Such data are essential for analyzing power quality, identifying operational patterns, and developing data-driven models for forecasting, control, and predictive maintenance. Yet, most existing open datasets lack the temporal granularity, measurement diversity, and machine-level detail needed to reflect the complexity of industrial environments. To address this gap, we present a large-scale, high-resolution dataset of industrial electricity measurements comprising more than 74 billion data points collected at 5-second resolution over up to seven years. The dataset includes 22 industrial machines and one photovoltaic system, with up to 190 measured quantities per device, including three-phase voltages and currents, active, reactive, and apparent power, harmonic spectra, total harmonic distortion, and fundamental waveform characteristics. In addition, the dataset is complemented by external metadata such as weather, electricity prices, and emission factors. This unique combination of long-term coverage, high sampling rate, and rich feature space enables insight into industrial energy dynamics and provides a robust foundation for advancing machine learning, digital twins, and intelligent energy management in industrial environments.