Search

Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data., (© 2024. The Author(s).)

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage.

Solid waste management (SWM) is a pressing concern and significant research topic that requires attention from citizens and government stakeholders. Most of the responsibility of waste management is on the municipal sector for its collection, reallocation, and reuse of other resources. The daily solid waste production is more than 54,850 tonnes in urban areas and is difficult to manage due to limited resources and different administrative and service issues. New technologies are playing their role in this area but how to integrate the technologies is still a question, especially for developing countries. This paper is divided into two main phases including a detailed investigation and a technological solution. In the first phase, the data is collected by using the qualitative method to investigate and identify the issues related to waste management. After a detailed investigation and results, the gap is identified by using statistical analysis and proposed a technological solution in the second phase. The technology-based solution is used to control and manage waste with a low-cost, fast, and manageable solution. The new sensor-based technologies, cellular networks, and social media are utilized to monitor the trash in the areas. The trash management department receives notification via cellular services to locate the dustbin when the dustbin reaches a maximum level so the department may send a waste collector vehicle to the relevant spot to collect waste. The smart and fast solution will connect all stakeholders in the community and reduce the cost and time and make the collection process faster. The experiment results indicated the issues and effectiveness of the proposed solution., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

As a strategy to coordinate inter-cell interference in cellular networks, a fractional frequency reuse (FFR) system is proposed, in which the frequency bandwidth is split into two orthogonal bands; users staying near the center of a FFR cell use the band with a frequency reuse (FR) factor of one (i.e., full FR), and users located close to the cell edge utilize the band with a FR factor greater than one (i.e., partial FR). Full FR coverage, which identifies full FR and partial FR regions (that is, near-center and near-edge regions) within a FFR cell, has a crucial effect on system performance. Some of the authors of this paper recently investigated the optimization of full FR coverage to maximize system throughput. They analytically showed that under the constraint of satisfying a specified target outage probability, the optimal full FR coverage is a non-increasing function of base station power when all base station powers in the cellular network are scaled at an equal rate. Interestingly, in this paper, it is proven that as the power of a single base station is scaled, the optimal full FR coverage in that cell is a non-decreasing function of base station power. Our results provide useful insight into the design of full FR coverage in relation to the transmit power of a base station. It gives a deeper understanding of the intricate relationship between important FFR system parameters of base station power and full FR coverage.

Patients who suffer from traumatic brain injury (TBI) often complain of learning and memory problems. Their symptoms are principally mediated by the hippocampus and the ability to adapt to stimulus, also known as neural plasticity. Therefore, one plausible injury mechanism is plasticity impairment, which currently lacks comprehensive investigation across TBI research. For these studies, we used a computational network model of the hippocampus that includes the dentate gyrus, CA3, and CA1 with neuron-scale resolution. We simulated mild injury through weakened spike-timing-dependent plasticity (STDP), which modulates synaptic weights according to causal spike timing. In preliminary work, we found functional deficits consisting of decreased firing rate and broadband power in areas CA3 and CA1 after STDP impairment. To address structural changes with these studies, we applied modularity analysis to evaluate how STDP impairment modifies community structure in the hippocampal network. We also studied the emergent function of network-based learning and found that impaired networks could acquire conditioned responses after training, but the magnitude of the response was significantly lower. Furthermore, we examined pattern separation, a prerequisite of learning, by entraining two overlapping patterns. Contrary to our initial hypothesis, impaired networks did not exhibit deficits in pattern separation with either population- or rate-based coding. Collectively, these results demonstrate how a mechanism of injury that operates at the synapse regulates circuit function., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Schumm, Gabrieli and Meaney.)

Background: Human mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology., Methods: In this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour., Results: Heterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population., Conclusions: High human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed., (© 2022. The Author(s).)

The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access network.

Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms.

In the last decades, new evidence on brain structure and function has been acquired by morphological investigations based on synergic interactions between biochemical anatomy approaches, new techniques in microscopy and brain imaging, and quantitative analysis of the obtained images. This effort produced an expanded view on brain architecture, illustrating the central nervous system as a huge network of cells and regions in which intercellular communication processes, involving not only neurons but also other cell populations, virtually determine all aspects of the integrative function performed by the system. The main features of these processes are described. They include the two basic modes of intercellular communication identified (i.e., wiring and volume transmission) and mechanisms modulating the intercellular signaling, such as cotransmission and allosteric receptor-receptor interactions. These features may also open new possibilities for the development of novel pharmacological approaches to address central nervous system diseases. This aspect, with a potential major impact on molecular medicine, will be also briefly discussed.

Introduction: During the COVID-19 pandemic, researchers have used Internet-based applications to conduct virtual group meetings, but this is not feasible in low-resource settings. In a community health research project in Bauchi State, Nigeria, COVID-19 restrictions precluded planned face-to-face meetings with community groups. We tested the feasibility of using cellular teleconferencing for these meetings., Methods: In an initial exercise, we used cellular teleconferencing to conduct six male and six female community focus group discussions. Informed by this experience, we conducted cellular teleconferences with 10 male and 10 female groups of community leaders, in different communities, to discuss progress with previously formulated action plans. Ahead of each teleconference call, a call coordinator contacted individual participants to seek consent and confirm availability. The coordinator connected the facilitator, the reporter, and the participants on each conference call, and audio-recorded the call. Each call lasted less than 1 h. Field notes and debriefing meetings with field teams supported the assessment of feasibility of the teleconference meetings., Results: Cellular teleconferencing was feasible and inexpensive. Using multiple handsets at the base allowed more participants in a call . Guidelines for facilitators and participants developed after the initial meetings were helpful, as were reminder calls ahead of the meeting. Connecting women participants was challenging. Facilitators needed extra practice to support group interactions without eye contact and body language signals., Conclusions: With careful preparation and training, cellular teleconferencing can be a feasible and inexpensive method of conducting group discussions in a low-resource setting., Competing Interests: Declaration of conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Before each user equipment (UE) can send data using the narrowband physical uplink shared channel (NPUSCH), each UE should periodically monitor a search space in the narrowband physical downlink control channel (NPDCCH) to decode a downlink control indicator (DCI) over narrowband Internet of Things (NB-IoT). This monitoring period, called the NPDCCH period in NB-IoT, can be flexibly adjusted for UEs with different channel qualities. However, because low-cost NB-IoT UEs operate in the half-duplex mode, they cannot monitor search spaces in NPDCCHs and transmit data in the NPUSCH simultaneously. Thus, as we observed, a percentage of uplink subframes will be wasted when UEs monitor search spaces in NPDCCHs, and the wasted percentage is higher when the monitored period is shorter. In this paper, to address this issue, we formulate the cross-cycled resource allocation problem to reduce the consumed subframes while satisfying the uplink data requirement of each UE. We then propose a cross-cycled uplink resource allocation algorithm to efficiently use the originally unusable NPUSCH subframes to increase resource utilization. Compared with the two resource allocation algorithms, the simulation results verify our motivation of using the cross-cycled radio resources to achieve massive connections over NB-IoT, especially for UEs with high channel qualities. The results also showcase the efficiency of the proposed algorithm, which can be flexibly applied for more different NPDCCH periods.

Introduction: Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions., Areas Covered: We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation., Expert Opinion: Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

The fifth generation (5G) of cellular networks improves the precision of user localization and provides the means to disclose location information to over-the-top (OTT) service providers. The network data analytics function (NWDAF) can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mobile network operators (MNOs) and OTT service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.

Metabolic engineering is the use of recombinant DNA technology, synthetic biology and genome editing to modify the cellular networks including metabolic, gene regulatory, and signaling networks of an organism. It can achieve the desirable goals such as enhanced production of metabolites, and improve the capability of biomanufacturing pharmaceuticals, biofuels and biochemicals as well as other biotechnology products. In order to comprehend the status of metabolic engineering in past 30 years, we published this special issue to review the progress and trends of metabolic engineering from the four aspects of overall development, key technologies, host engineering and product engineering, respectively, for laying the foundation for the further development of metabolic engineering.

Background: Wireless links are fast becoming the key communication mode. However, as compared to the wired link, their characteristics make the traffic prone to time- and location-dependent signal attenuation, noise, fading, and interference that result in time varying channel capacities and link error rate. Scheduling algorithms play an important role in wireless links to guarantee quality of service (QoS) parameters such as throughput, delay, jitter, fairness and packet loss rate. The scheduler has vital importance in current as well as future cellular communications since it assigns resource block (RB) to different users for transmission. Scheduling algorithm makes a decision based on the information of link state, number of sessions, reserved rates and status of the session queues. The information required by a scheduler implemented in the base station can easily be collected from the downlink transmission., Methods: This paper reflects on the importance of schedulers for future wireless communications taking LTE-A networks as a case study. It compares the performance of four well-known scheduling algorithms including round robin (RR), best channel quality indicator (BCQI), proportional fair (PF), and fractional frequency reuse (FFR). The performance of these four algorithms is evaluated in terms of throughput, fairness index, spectral efficiency and overall effectiveness. System level simulations have been performed using a MATLAB based LTE-A Vienna downlink simulator., Results: The results show that the FFR scheduler is the best performer among the four tested algorithms. It also exhibits flexibility and adaptability for radio resource assignment., Competing Interests: Masood Ur-Rehman is an Academic Editor for PeerJ., (©2021 Ashfaq et al.)

The year 2020 was marked by the emergence of the COVID-19 pandemic. After months of uncontrolled spread worldwide, a clear conclusion is that controlling the mobility of the general population can slow down the propagation of the pandemic. Tracking the location of the population enables better use of mobility limitation policies and the prediction of potential hotspots, as well as improved alert services to individuals that may have been exposed to the virus. With mobility in their core functionality and a high degree of penetration of mobile devices within the general population, cellular networks are an invaluable asset for this purpose. This paper shows an overview of the possibilities offered by cellular networks for the massive tacking of the population at different levels. The major privacy concerns are also reviewed and a specific use case is shown, correlating mobility and number of cases in the province of Málaga (Spain).

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

While both chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are multifactorial disorders characterized by distinct clinical and pathological features, their commonalities and differences have not been fully elucidated. We sought to investigate the preventive roles of tetraspanins Cd151 and Cd9 -that are involved in diverse cellular processes in lung pathophysiology- in pulmonary fibrosis and emphysema, respectively, and to obtain a deeper understanding of their underlying molecular mechanisms toward facilitating improved therapeutic outcomes. Using an integrative approach, we examined the transcriptomic changes in the lungs of Cd151- and Cd9-deficient mice using functional-enrichment-analysis, pathway-perturbation-analysis and protein-protein-interaction (PPI) network analysis. Circadian-rhythm, extracellular-matrix (ECM), cell-adhesion and inflammatory responses and associated factors were prominently influenced by Cd151-deletion. Conversely, cellular-junctions, focal-adhesion, vascular-remodeling, and TNF-signaling were deeply impacted by Cd9-deletion. We also highlighted a "common core" of factors and signaling cascades that underlie the functions of both Cd151 and Cd9 in lung pathology. Circadian dysregulation following Cd151-deletion seemingly facilitated progressive fibrotic lung phenotype. Conversely, TGF-β signaling attenuation and TNF-signaling activation emerged as potentially novel functionaries of Cd9-deletion-induced emphysema. Our findings offer promising avenues for developing novel therapeutic treatments for pulmonary fibrosis and emphysema., Competing Interests: YK was employed by the company Sumitomo Dainippon Pharma Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Tripathi, Itoh, Takeda, Tsujino, Kondo, Kumanogoh and Mizuguchi.)

Two-dimensional (2D) materials remain highly interesting for assembling three-dimensional (3D) structures, amongst others, in the form of macroscopic hydrogels. Herein, we present a novel approach for inducing chemical inter-sheet crosslinks via an ethylenediamine mediated reaction between Ti 3 C 2 T x and graphene oxide in order to obtain a reduced graphene oxide-MXene (rGO-MXene) hydrogel. The composite hydrogels are hydrophilic with a stiffness of ~20 kPa. They also possess a unique inter-connected porous architecture, which led to a hitherto unprecedented ability of human cells across three different types, epithelial adenocarcinoma, neuroblastoma and fibroblasts, to form inter-connected three-dimensional networks. The attachments of the cells to the rGO-MXene hydrogels were superior to those of the sole rGO-control gels. This phenomenon stems from the strong affinity of cellular protrusions (neurites, lamellipodia and filopodia) to grow and connect along architectural network paths within the rGO-MXene hydrogel, which could lead to advanced control over macroscopic formations of cellular networks for technologically relevant bioengineering applications, including tissue engineering and personalized diagnostic networks-on-chip. STATEMENT OF SIGNIFICANCE: Conventional hydrogels are made of interconnected polymeric fibres. Unlike conventional case, we used hydrothermal and chemical approach to form interconnected porous hydrogels made of two-dimensional flakes from graphene oxide and metal carbide from a new family of MXenes (Ti 3 C 2 T x ). This way, we formed three-dimensional porous hydrogels with unique porous architecture of well-suited chemical surfaces and stiffness. Cells from three different types cultured on these scaffolds formed extended three-dimensional networks - a feature of extended cellular proliferation and pre-requisite for formation of organoids. Considering the studied 2D materials typically constitute materials exhibiting enhanced supercapacitor performances, our study points towards better understanding of design of tissue engineering materials for the future bioengineering fields including personalized diagnostic networks-on-chip, such as artificial heart actuators., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)

This paper considers a downlink cellular network where multi-antenna base stations (BSs) simultaneously serve their associated multi-antenna users. Each BS is distributed according to a homogeneous Poisson point process and uses zero-forcing beamforming for spatial division multiplexing with partial channel state information (CSI). During downlink transmission, each user combines the multiple antenna outputs and quantizes the CSI to feed back to its associated BS. Specifically, this paper focuses on antenna combining at the receiver. Conventional quantization-based combining (QBC) effectively reduces the quantization error; however, inter-cell interference in the cellular networks degrades the QBC gain. This degradation is analyzed using a spherical-cap approximation of vector quantization (SCVQ). From the SCVQ, the ergodic spectral efficiency and the optimal number of feedback bits are investigated, and it is shown that the QBC degrades the gain of the effective channel. To address this problem, an optimization solution is proposed that selects the antenna combining to maximize the spectral efficiency. The solution is also derived by considering the expected beamforming vectors of other cells. It is demonstrated by simulation that the proposed solution outperforms the conventional methods.

Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink-downlink resources and guaranteeing the cellular users' (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn-Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.

Lignin accumulates in the cell walls of specialized cell types to enable plants to stand upright and conduct water and minerals, withstand abiotic stresses, and defend themselves against pathogens. These functions depend on specific lignin concentrations and subunit composition in different cell types and cell wall layers. However, the mechanisms controlling the accumulation of specific lignin subunits, such as coniferaldehyde, during the development of these different cell types are still poorly understood. We herein validated the Wiesner test (phloroglucinol/HCl) for the restrictive quantitative in situ analysis of coniferaldehyde incorporation in lignin. Using this optimized tool, we investigated the genetic control of coniferaldehyde incorporation in the different cell types of genetically-engineered herbaceous and woody plants with modified lignin content and/or composition. Our results demonstrate that the incorporation of coniferaldehyde in lignified cells is controlled by (a) autonomous biosynthetic routes for each cell type, combined with (b) distinct cell-to-cell cooperation between specific cell types, and (c) cell wall layer-specific accumulation capacity. This process tightly regulates coniferaldehyde residue accumulation in specific cell types to adapt their property and/or function to developmental and/or environmental changes., (Copyright © 2020 Blaschek, Champagne, Dimotakis, Nuoendagula, Decou, Hishiyama, Kratzer, Kajita and Pesquet.)

In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity., Competing Interests: The authors declare no conflict of interest.

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

In the vehicular ad-hoc networks (VANETs), wireless access in vehicular environments (WAVE) as the core networking technology is suitable for supporting safety-critical applications, but it is difficult to guarantee its performance when transmitting non-safety data, especially high volumes of data, in a multi-hop manner. Therefore, to provide non-safety applications effectively and reliably for users, we propose a hybrid V2V communication system (HVCS) using hierarchical networking architecture: a centralized control model for the establishment of a fast connection and a local data propagation model for efficient and reliable transmissions. The centralized control model had the functionality of node discovery, local ad-hoc group (LAG) formation, a LAG owner (LAGO) determination, and LAG management. The local data propagation indicates that data are transmitted only within the LAG under the management of the LAGO. To support the end-to-end multi-hop transmission over V2V communication, vehicles outside the LAG employ the store and forward model. We designed three phases consisting of concise device discovery (CDD), concise provisioning (CP), and data transmission, so that the HVCS is highly efficient and robust on the hierarchical networking architecture. Under the centralized control, the phase of the CDD operates to improve connection establishment time, and the CP is to simplify operations required for security establishment. Our HVCS is implemented as a two-tier system using a traffic controller for centralized control using cellular networks and a smartphone for local data propagation over Wi-Fi Direct. The HVCS' performance was evaluated using Veins, and compared with WAVE in terms of throughput, connectivity, and quality of service (QoS). The effectiveness of the centralized control was demonstrated in comparative experiments with Wi-Fi Direct. The connection establishment time measured was only 0.95 s for the HVCS. In the case of video streaming services through the HVCS, about 98% of the events could be played over 16 frames per second. The throughput for the streaming data was between 74% to 81% when the vehicle density was over 50%. We demonstrated that the proposed system has high throughput and satisfies the QoS of streaming services even though the end-to-end delay is a bit longer when compared to that of WAVE.

In this data article, we report real-world data on multichannel connectivity and videotransmission carried on commercial 3G/4G networks in the region of Skåne, southern Sweden. The data reported here complement the research article "Technical feasibility and ambulance nurses' view of a digital telemedicine system in pre-hospital stroke care - A pilot study" (1). The dataset was originally collected as part of a project aimed to test in a clinical setting the quality and usefulness of a linked image and sound transmission in the prehospital assessment of patients with suspected stroke. The project built on previous studies indicating that using high-quality telemedicine in stroke cases is feasible and has already impacted local stroke care Schwamm et al., 2009. In addition, studies support the hypothesis that stroke telemedicine consultations, compared with telephone-only, result in more accurate decision-making Demaerschalk et al., 2012. Cellular networks for 3/4G networks have been greatly improved, a prerequisite for the use of these networks for e. g. medical applications. However, connectivity maps for planning purposes are usually based on theoretical values that do not consider smaller features of the terrain such as large trees, hills, rocks etc. and that may interfere with connectivity. To leverage several networks, multichannel devices have been developed that split the original transmission onto several independent channels and recombine the transmission on the receiver side. This setup allows to increase the available bandwidth and introduces at the same time an element of redundancy, provided that several providers with independent networks are used.

Cellular-based networks keep large buffers at base stations to smooth out the bursty data traffic, which has a negative impact on the user's Quality of Experience (QoE). With the boom of smart vehicles and phones, this has drawn growing attention. For this paper, we first conducted experiments to reveal the large delays, thus long flow completion time (FCT), caused by the large buffer in the cellular networks. Then, a receiver-side transmission control protocol (TCP) countermeasure named Delay-based Flow Control algorithm with Service Differentiation (DFCSD) was proposed to target interactive applications requiring high throughput and low delay in cellular networks by limiting the standing queue size and decreasing the amount of packets that are dropped in the eNodeB in Long Term Evolution (LTE). DFCSD stems from delay-based congestion control algorithms but works at the receiver side to avoid the performance degradation of the delay-based algorithms when competing with loss-based mechanisms. In addition, it is derived based on the TCP fluid model to maximize the network utility. Furthermore, DFCSD also takes service differentiation into consideration based on the size of competing flows to shorten their completion time, thus improving user QoE. Simulation results confirmed that DFCSD is compatible with existing TCP algorithms, significantly reduces the latency of TCP flows, and increases network throughput.

The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method.

The dynamics of eukaryotic systems provide us with a signature of their response to stress, perturbations, or sustained, cyclic, or periodic variations and fluctuations. Studying the dynamic behavior of such systems is therefore elemental in achieving a mechanistic understanding of cellular behavior. This conceptual chapter discusses some of the key aspects that need to be considered in the study of dynamic responses of eukaryotic systems, in particular of eukaryotic networks. However, it does not aim to provide an exhaustive evaluation of the existing methodologies. The discussions in the chapter primarily relate to the cellular networks of eukaryotes and essentially leave higher dynamic community structures such as social networks, epidemic spreading, or ecological networks out of the scope of this argument.

Heat shock protein Hsp70 (Hsp70) is found on the cell surface of a large variety of human and mouse tumor cell types including U87, GL261 glioblastoma, and 4T1 mammary carcinoma cells. We studied the role of membrane-bound Hsp70 (mHsp70) in the formation of cell-to-cell connections via tunneling nanotubes (TNTs) using live-cell STED nanoscopy. This technique allows the visualization of microstructures in the 100-nm range in the living cells. We could show that the presence of tumor-derived mHsp70 in TNTs with a diameter ranging from 120 to 140 nm predominantly originates from cholesterol-rich-microdomains containing the lipid compound globoyltriaosylceramide (Gb3). Under non-stress conditions, Hsp70 and Gb3 are structurally clustered in the membrane of TNTs of tumor cells that showed tumor type specific variations in the amount of cell-to-cell connection networks. Furthermore depletion of cholesterol and ionizing radiation as a stress factor results in a complete loss of Hsp70-containing TNTs.

In mammals, circadian rhythms, such as sleep/wake cycles, are regulated by the central circadian clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN consists of thousands of individual neurons, which exhibit circadian rhythms. They synchronize with each other and produce robust and stable oscillations. Although several neurotransmitters are expressed in the SCN, almost all SCN neurons are γ-amino butyric acid (GABA)-ergic. Several studies have attempted to understand the roles of GABA in the SCN; however, precise mechanisms of the action of GABA in the SCN are still unclear. GABA exhibits excitatory and/or inhibitory characteristics depending on the circadian phase or region in the SCN. It can both synchronize and destabilize cellular circadian rhythms in individual SCN cells. Differing environmental light conditions, such as a long photoperiod, result in the decoupling of circadian oscillators of the dorsal and ventral SCN. This is due to high intracellular chloride concentrations in the dorsal SCN. Because mice with functional GABA deficiency, such as vesicular GABA transporter- and glutamate decarboxylase-deficient mice, are neonatal lethal, research has been limited to pharmacological approaches. Furthermore, different recording methods have been used to understand the roles of GABA in the SCN. The excitability of GABAergic neurons also changes during the postnatal period. Although there are technical difficulties in understanding the functions of GABA in the SCN, technical developments may help uncover new roles of GABA in circadian physiology and behavior.

The bulk transport of molecules through plant tissues underpins growth and development. The stem acts as a conduit between the upper and low domains of the plant, facilitating transport of solutes and water from the roots to the shoot system, and sugar plus other elaborated metabolites towards the non-photosynthetic organs. In order to perform this function efficiently, the stem needs to be optimized for transport. This is achieved through the formation of vasculature that connects the whole plant but also through connectivity signatures that reduce path length distributions outside the vascular system. This protocol was devised to characterize how cell connectivity affects the bulk flow of molecules traversing the stem. This is achieved by exposing young seedlings to fluorescein, for which no specific transporter is assumed to be present in A. thaliana , and assessing the relative concentration of this fluorescent compound in individual cells of the embryonic stem (hypocotyl) using confocal microscopy and quantitative 3D image analysis after a given exposure time., (Copyright © The Authors; exclusive licensee Bio-protocol LLC.)

In cellular networks, each mobile station adjusts its power level under control of its base station, i.e., through uplink transmit power control, which is essential to reach desired signal-to-interference-plus-noise ratio (SINR) at the base station and to limit inter-cell interference. The optimal levels of transmit power in a network depend on path loss, shadowing, and multipath fading, as well as the network configuration. However, since path loss is distance dependent and the cell association distances are correlated due to the cell association policies, the performance analysis of the uplink transmit power control is very complicated. Consequently, the impact of a specific power control algorithm on network performance is hard to quantify. In this paper, we analyze three uplink transmit power control schemes. We assume the standard power-law path loss and composite Rayleigh-lognormal fading. Using stochastic geometry tools, we derive the cumulative distribution function and the probability density function of the uplink transmit power and the resulting network coverage probability. It is shown that the coverage is highly dependent on the severity of shadowing, the power control scheme, and its parameters, but invariant of the density of deployment of base stations when the shadowing is mild and power control is fractional. At low SINRs, compensation of both path loss and shadowing improves the coverage. However, at high SINRs, compensating for path loss only improves coverage. Increase in the severity of shadowing significantly reduces the coverage., Competing Interests: Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Given its prevalence and social impact, Autism Spectrum Disorder (ASD) is drawing much interest. Molecular basis of ASD is heterogeneous and only partially known. Many factors, including disorders comorbid with ASD, like TS (Tourette Syndrome), complicate ASD behavior-based diagnosis and make it vulnerable to bias. To further investigate ASD etiology and to identify potential biomarkers to support its precise diagnosis, we used TaqMan Low Density Array technology to profile serum miRNAs from ASD, TS, and TS+ASD patients, and unaffected controls (NCs). Through validation assays in 30 ASD, 24 TS, and 25 TS+ASD patients and 25 NCs, we demonstrated that miR-140-3p is upregulated in ASD vs.: NC, TS, and TS+ASD (Tukey's test, p -values = 0.03, = 0.01, < 0.0001, respectively). ΔCt values for miR-140-3p and YGTSS (Yale Global Tic Severity Scale) scores are positively correlated (Spearman r = 0.33; Benjamini-Hochberg p = 0.008) and show a linear relationship ( p = 0.002). Network functional analysis showed that nodes controlled by miR-140-3p, especially CD38 and NRIP1 which are its validated targets, are involved in processes convergingly dysregulated in ASD, such as synaptic plasticity, immune response, and chromatin binding. Biomarker analysis proved that serum miR-140-3p can discriminate among: (1) ASD and NC (Area under the ROC curve, AUC: 0.70; sensitivity: 63.33%; specificity: 68%); (2) ASD and TS (AUC: 0.72; sensitivity: 66.66%; specificity: 70.83%); (3) ASD and TS+ASD (AUC: 0.78; sensitivity: 73.33%; specificity: 76%). Characterization of miR-140-3p network would contribute to further clarify ASD etiology. Serum miR-140-3p could represent a potential non-invasive biomarker for ASD, easy to test through liquid biopsy.

The advent of systems biology has convincingly demonstrated that the integration of experiments and dynamic modelling is a powerful approach to understand the cellular network biology. Here we present experimental and computational protocols that are necessary for applying this integrative approach to the quantitative studies of receptor tyrosine kinase (RTK) signaling networks. Signaling by RTKs controls multiple cellular processes, including the regulation of cell survival, motility, proliferation, differentiation, glucose metabolism, and apoptosis. We describe methods of model building and training on experimentally obtained quantitative datasets, as well as experimental methods of obtaining quantitative dose-response and temporal dependencies of protein phosphorylation and activities. The presented methods make possible (1) both the fine-grained modeling of complex signaling dynamics and identification of salient, course-grained network structures (such as feedback loops) that bring about intricate dynamics, and (2) experimental validation of dynamic models.

The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.

Urban Automation Networks (UANs) are being deployed worldwide in order to enable Smart City applications. Given the crucial role of UANs, as well as their diversity, it is critically important to assess their properties and trade-offs. This article introduces the requirements and challenges for UANs, characterizes the main current and emerging UAN paradigms, provides guidelines for their design and/or choice, and comparatively examines their performance in terms of a variety of parameters including coverage, power consumption, latency, standardization status and economic cost.

Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.

Under acute perturbations from the outside, cells can trigger self-defensive mechanisms to fight against genome stress. To investigate the cellular response to continuous ion radiation (IR), a dynamic model for p53 stress response networks at the cellular level is proposed. The model can successfully be used to simulate the dynamic processes of double-strand breaks (DSBs) generation and their repair, switch-like ataxia telangiectasia mutated (ATM) activation, oscillations occurring in the p53-MDM2 feedback loop, as well as toxins elimination triggered by p53 stress response networks. Especially, the model can predict the plausible outcomes of cellular response under different IR dose regimes., (Copyright © 2009 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Ltd.)