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In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.

Extracellular vesicles (EVs) are nanoscale vesicles secreted into the extracellular space by all cell types, including neurons and astrocytes in the brain. EVs play pivotal roles in physiological and pathophysiological processes such as waste removal, cell-to-cell communication and transport of either protective or pathogenic material into the extracellular space. Here we describe a detailed protocol for the reliable and consistent isolation of EVs from both murine and human brains, intended for anyone with basic laboratory experience and performed in a total time of 27 h. The method includes a mild extracellular matrix digestion of the brain tissue, a series of filtration and centrifugation steps to purify EVs and an iodixanol-based high-resolution density step gradient that fractionates different EV populations, including mitovesicles, a newly identified type of EV of mitochondrial origin. We also report detailed downstream protocols for the characterization and analysis of brain EV preparations using nanotrack analysis, electron microscopy and western blotting, as well as for measuring mitovesicular ATP kinetics. Furthermore, we compared this novel iodixanol-based high-resolution density step gradient to the previously described sucrose-based gradient. Although the yield of total EVs recovered was similar, the iodixanol-based gradient better separated distinct EV species as compared with the sucrose-based gradient, including subpopulations of microvesicles, exosomes and mitovesicles. This technique allows quantitative, highly reproducible analyses of brain EV subtypes under normal physiological processes and pathological brain conditions, including neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. This protocol describes the isolation from brain tissue of extracellular vesicle subpopulations, including microvesicles, exosomes and mitochondria-derived mitovesicles, using a high-resolution (iodixanol) density step gradient. Extracellular vesicle characterization and analysis are also presented.

Techniques for reprogramming somatic cells create new opportunities for drug screening, disease modeling, artificial organ development, and cell therapy. The development of reprogramming techniques has grown exponentially since the discovery of induced pluripotent stem cells (iPSCs) by the transduction of four factors (OCT3/4, SOX2, c-MYC, and KLF4) in mouse embryonic fibroblasts. Initial studies on iPSCs led to direct-conversion techniques using transcription factors expressed mainly in target cells. However, reprogramming transcription factors with a virus risks integrating viral DNA and can be complicated by oncogenes. To address these problems, many researchers are developing reprogramming methods that use clinically applicable small molecules and growth factors. This review summarizes research trends in reprogramming cells using small molecules and growth factors, including their modes of action.Regenerative medicine: Harnessing the reprogramming power of small moleculesThe reprogramming of cells using small molecules to generate viable, safe stem-cell populations could transform stem-cell therapies, disease modeling and artificial organ development. Existing ways of reprogramming cells to generate stem cells carry risks, because the methods used often involve using viral DNA components or oncogenes, genes with the potential to turn cells into tumour cells. Safer, inexpensive alternatives are sought by scientists, and the efficient reprogramming of cells using small molecules and growth factors shows promise. Dongho Choi and co-workers at Hanyang University College of Medicine in Seoul, South Korea, reviewed recent research highlighting how small molecules including chemical compounds, plant derivatives and certain approved drugs are being used effectively to create different stem-cell populations. Recent successes are also contributing valuable insights into how stem cells differentiate into different cell types.

Background MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally. Results In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance. Conclusion We found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available. 3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.

Background Experts in peptide:MHC binding studies are often able to estimate the impact of a single residue substitution based on a heuristic understanding of amino acid similarity in an experimental context. Our aim is to quantify this measure of similarity to improve peptide:MHC binding prediction methods. This should help compensate for holes and bias in the sequence space coverage of existing peptide binding datasets. Results Here, a novel amino acid similarity matrix (PMBEC) is directly derived from the binding affinity data of combinatorial peptide mixtures. Like BLOSUM62, this matrix captures well-known physicochemical properties of amino acid residues. However, PMBEC differs markedly from existing matrices in cases where residue substitution involves a reversal of electrostatic charge. To demonstrate its usefulness, we have developed a new peptide:MHC class I binding prediction method, using the matrix as a Bayesian prior. We show that the new method can compensate for missing information on specific residues in the training data. We also carried out a large-scale benchmark, and its results indicate that prediction performance of the new method is comparable to that of the best neural network based approaches for peptide:MHC class I binding. Conclusion A novel amino acid similarity matrix has been derived for peptide:MHC binding interactions. One prominent feature of the matrix is that it disfavors substitution of residues with opposite charges. Given that the matrix was derived from experimentally determined peptide:MHC binding affinity measurements, this feature is likely shared by all peptide:protein interactions. In addition, we have demonstrated the usefulness of the matrix as a Bayesian prior in an improved scoring-matrix based peptide:MHC class I prediction method. A software implementation of the method is available at: http://www.mhc-pathway.net/smmpmbec .

Interactions between monatomic ions and water molecules are fundamental to understanding the hydration of complex polyatomic ions and ionic process. Among the simplest and well-established ion-related reactions is dissolution of salt in water, which is an endothermic process requiring an increase in entropy. Extensive efforts have been made to date; however, most studies at single-ion level have been limited to theoretical approaches. Here, we demonstrate the salt dissolution process by manipulating a single water molecule at an under-coordinated site of a sodium chloride film. Manipulation of molecule in a controlled manner enables us to understand ion–water interaction as well as dynamics of water molecules at NaCl interfaces, which are responsible for the selective dissolution of anions. The water dipole polarizes the anion in the NaCl ionic crystal, resulting in strong anion–water interaction and weakening of the ionic bonds. Our results provide insights into a simple but important elementary step of the single-ion chemistry, which may be useful in ion-related sciences and technologies. The strong ionic bond in salt is broken by electrostatic interactions with water, but direct observation at the level of a single ion is challenging. Here, the authors have visualized the preferential dissolution of an anion by manipulating a single water molecule.

Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.

Macrophages engulf and destroy pathogens (phagocytosis) and apoptotic cells (efferocytosis), and can subsequently initiate adaptive immune responses by presenting antigens derived from engulfed materials. Both phagocytosis and efferocytosis share a common degradative pathway in which the target is engulfed into a membrane-bound vesicle, respectively, termed the phagosome and efferosome, where they are degraded by sequential fusion with endosomes and lysosomes. Despite this shared maturation pathway, macrophages are immunogenic following phagocytosis but not efferocytosis, indicating that differential processing or trafficking of antigens must occur. Mass spectrometry and immunofluorescence microscopy of efferosomes and phagosomes in macrophages demonstrated that efferosomes lacked the proteins required for antigen presentation and instead recruited the recycling regulator Rab17. As a result, degraded materials from efferosomes bypassed the MHC class II loading compartment via the recycling endosome – a process not observed in phagosomes. Combined, these results indicate that macrophages prevent presentation of apoptotic cell-derived antigens by preferentially trafficking efferocytosed, but not phagocytosed, materials away from the MHC class II loading compartment via the recycling endosome pathway.

Targeted noninvasive control of the nervous system and end-organs may enable safer and more effective treatment of multiple diseases compared to invasive devices or systemic medications. One target is the cholinergic anti-inflammatory pathway that consists of the vagus nerve to spleen circuit, which has been stimulated with implantable devices to improve autoimmune conditions such as rheumatoid arthritis. Here we report that daily noninvasive ultrasound (US) stimulation targeting the spleen significantly reduces disease severity in a mouse model of inflammatory arthritis. Improvements are observed only with specific parameters, in which US can provide both protective and therapeutic effects. Single cell RNA sequencing of splenocytes and experiments in genetically-immunodeficient mice reveal the importance of both T and B cell populations in the anti-inflammatory pathway. These findings demonstrate the potential for US stimulation of the spleen to treat inflammatory diseases. Modulation of the cholinergic pathway and spleen function can reduce inflammation with invasive implants. Here, the authors show that non-invasive ultrasound stimulation of the spleen reduces disease severity in a mouse model of inflammatory arthritis, partly via altering B and T cell function.

Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.