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Purpose: To evaluate the outcome of a routine postoperative endoscopic micro-debridement of granulation tissue after stentless transnasal endoscopic repair of choanal atresia (CA). Methods: This prospective case series included congenital CA patients who underwent stentless transnasal endoscopic repair, followed by an endoscopic second look and micro-debridement of granulation tissue at 1–2 weeks post-repair. Patients were followed every three months for assessment of nasal airway symptoms and objective evaluation by flexible nasolaryngoscopy. Results: Sixteen CA patients (8 bilateral and 8 unilateral) underwent surgical repair (12 primary and 4 revisions). The median age was 13 days (range 1 day–6 months) in bilateral and 3 years (range 7 months–15 years) in unilateral atresia. The mean follow-up was 1.5 years (range 1 year–3 years). In primary procedures, the obstruction was bony-membranous in 7 cases and bony in 5 cases. The mean interval time between the CA repair and re-examination was 10.75 days (range 6–18 days). Clinically significant neochoanal restenosis was not encountered. Conclusions: Re-examination under general anesthesia with endoscopic micro-debridement of granulation tissue is a safe, potentially effective adjunct when done during the proliferative phase of neochoanal wound healing. This procedure might help in maintaining neochoanal patency by remodeling tissue healing process. Large-scale, long-term cohort studies are imperative. [ABSTRACT FROM AUTHOR]

Cell-free massive MIMO systems have promising data rate and coverage gains. These systems, however, typically rely on fiber based fronthaul for the communication between the central processing unit and the distributed access points (APs), which increases the infrastructure cost and installation complexity. To address these challenges, this paper proposes two architectures for cell-free massive MIMO systems based on wireless fronthaul that is operating at a higher-band compared to the access links. These dual-band architectures ensure high data rate fronthaul while reducing the infrastructure cost and enhancing the deployment flexibility and adaptability. To investigate the achievable data rates with the proposed architectures, we formulate the end-to-end data rate optimization problem accounting for the various practical aspects of the fronthaul and access links. Then, we develop a low-complexity yet efficient joint beamforming and resource allocation solution for the proposed architectures based on user-centric AP grouping. With this solution, we show that the proposed architectures can achieve comparable data rates to those obtained with optical fiber-based fronthaul under realistic assumptions on the fronthaul bandwidth, hardware constraints, and deployment scenarios. This highlights a promising path for realizing the cell-free massive MIMO gains in practice while reducing the infrastructure and deployment overhead. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data transmission. Current beam codebooks, however, generally consist of a large number of narrow beams that scan all possible directions, leading to large training overhead. Further, these codebooks do not normally account for hardware impairments or possible non-uniform array geometries, and their calibration process is expensive. To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics. This is done by designing a novel complex-valued neural network architecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints. This model learns the codebook beams through online and self-supervised training avoiding the need for explicit channel state information. This respects the practical situations where the channel is imperfect or hard to obtain. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which reduce the training overhead and improve the robustness against possible hardware impairments. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. These pre-defined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. This leads to large codebook sizes with high beam training overhead which makes it hard for these systems to support highly mobile applications. To overcome these limitations, this paper develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on the receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, RF hardware, or user positions. To reduce the learning time, the proposed model designs a novel Wolpertinger-variant architecture that is capable of efficiently searching the large discrete action space. The proposed learning framework respects the RF hardware constraints such as the constant-modulus and quantized phase shifter constraints. Simulation results confirm the ability of the developed framework to learn near-optimal beam patterns for line-of-sight (LOS), non-LOS (NLOS), mixed LOS/NLOS scenarios and for arrays with hardware impairments without requiring any channel knowledge. [ABSTRACT FROM AUTHOR]

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user’s movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation. [ABSTRACT FROM AUTHOR]

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection. [ABSTRACT FROM AUTHOR]

The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This article presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by red-green-blue (RGB) cameras deployed at the base stations. In particular, the article proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. This is mainly achieved via a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than ${\sim} 90\%$ of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches 87%. This highlights a promising direction for enabling high reliability and low latency in future wireless networks. [ABSTRACT FROM AUTHOR]

Objectives/hypothesis: The anatomy of children with severe Pierre Robin sequence can present a challenge for direct laryngoscopy and intubation. Advanced techniques including flexible fiberoptic laryngoscopic intubation have been described but require highly specialized skill and equipment. Rigid video laryngoscopy is more accessible but has not been described in this population.Study Design: Retrospective cohort study.Methods: A retrospective review was completed at a tertiary care center of all children between January 2016 and March 2020 with Pierre Robin sequence who underwent a mandibular distraction osteogenesis procedure. Intubation events were collected, and a descriptive analysis was performed. A univariate logistic regression model was applied to direct laryngoscopy and flexible fiberoptic laryngoscopy with rigid video laryngoscopy as a reference.Results: Twenty-five patients were identified with a total of 56 endotracheal events. All patients were successfully intubated. Direct laryngoscopy was successful at first intubation attempt in 47.3% (9/19) of events. Six direct laryngoscopy events required switching to another device. Rigid video laryngoscopy was successful at first intubation attempt in 80.5% (29/36) of events. Two cases required switching to another device. Flexible fiberoptic laryngoscopy was found successful at first intubation attempt in 88.9% (8/9) of events. Direct laryngoscopy was 4 times more likely to fail first intubation attempt when compared to rigid video laryngoscopy (P < .05). There was no significant difference between rigid video laryngoscopy and flexible fiberoptic laryngoscopy for intubation.Conclusions: For children with Pierre Robin sequence rigid video laryngoscopy should be considered as a first attempt intubation device both in the operating room and for emergent situations.Level Of Evidence: 4 Laryngoscope, 131:1647-1651, 2021. [ABSTRACT FROM AUTHOR]

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulation results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data to assist the current wireless communications. [ABSTRACT FROM AUTHOR]

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Regression modeling has received much attention in several science fields. Firefly algorithm is one of the recently ef- ficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, chaotic firefly algorithm is proposed to perform variable selection for gamma regression model. A real data application related to the chemometrics is conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods. [ABSTRACT FROM AUTHOR]

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty. [ABSTRACT FROM AUTHOR]

Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work has focused on extracting spatial channel characteristics at the sub-6 GHz band and then use them to reduce the mmWave beam training overhead. This approach still requires beam refinement at mmWave and does not normally account for the different dielectric properties at the different bands. In this paper, we first prove that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and blockage status directly from the sub-6 GHz channel. These mapping functions, however, are hard to characterize analytically which motivates exploiting deep neural network models to learn them. For that, we prove that a large enough neural network can predict mmWave beams and blockages with success probabilities that can be made arbitrarily close to one. Then, we develop a deep learning model and empirically evaluate its beam/blockage prediction performance using a publicly available dataset. The results show that the proposed solution can predict the mmWave blockages with more than 90% success probability and can predict the optimal mmWave beams to approach the upper bounds while requiring no beam training overhead. [ABSTRACT FROM AUTHOR]

Employing low-resolution analog-to-digital converters (ADCs) for millimeter-wave receivers with large antenna arrays provides an opportunity to efficiently reduce power consumption of the receiver. Reducing ADC resolution, however, results in performance degradation due to non-negligible quantization error. In addition, the large number of radio frequency chains is still not desirable. Accordingly, conventional low-resolution ADC systems require more efficient designs to minimize the cost and complexity while maximizing performance. In this article, we discuss advanced low-resolution ADC receiver architectures that further improve the spectral and energy efficiency trade-off. To reduce both the numbers of RF chains and ADC bits, hybrid analog and digital beamforming is jointly considered with low-resolution ADCs. We explore the challenges in designing such receivers and present key insights on how the advanced architectures overcome such challenges. As alternative low-resolution ADC receivers, we also introduce receivers with learning-based detection. The receiver does not require explicit channel estimation, and thus is suitable for one-bit ADC systems. Finally, future challenges and research issues are discussed. [ABSTRACT FROM AUTHOR]

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time. [ABSTRACT FROM AUTHOR]

The Liu estimator has consistently been demonstrated to be an attractive shrinkage method for reducing the effects of multicollinearity. The Poisson regression model is a well-known model in applications when the response variable consists of count data. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator (MLE) of the Poisson regression coefficients. To address this problem, a Poisson Liu estimator has been proposed by numerous researchers. In this paper, a Jackknifed Liu-type Poisson estimator (JPLTE) is proposed and derived. The idea behind the JPLTE is to decrease the shrinkage parameter and, therefore, improve the resultant estimator by reducing the amount of bias. Our Monte Carlo simulation results suggest that the JPLTE estimator can bring significant improvements relative to other existing estimators. In addition, the results of a real application demonstrate that the JPLTE estimator outperforms both the Poisson Liu estimator and the maximum likelihood estimator in terms of predictive performance. [ABSTRACT FROM AUTHOR]

Massive MIMO systems achieve high sum spectral efficiency by simultaneously serving large numbers of users. In time division duplexing systems, however, the reuse of uplink training pilots among cells results in channel estimation errors, which causes downlink inter-cell interference. Handling this interference is challenging due to the large channel dimensionality and the high complexity associated with implementing large precoding/combining matrices. In this paper, we propose multi-layer precoding to enable efficient and low-complexity operation in full-dimensional massive MIMO, where a large number of antennas are used in two dimensions. In multi-layer precoding, the precoding matrix of each base station is written as a product of a number of precoding matrices. Multi-layer precoding: 1) leverages the directional characteristics of large-scale MIMO channels to manage inter-cell interference with low channel knowledge requirements and 2) allows for an efficient implementation using hybrid analog/digital architectures. We present and analyze a specific multi-layer precoding design for full-dimensional massive MIMO systems. The asymptotic optimality of the proposed design is then proved for some special yet important channels. Numerical simulations verify the analytical results and illustrate the potential gains of multi-layer precoding compared with other multi-cell precoding solutions. [ABSTRACT FROM PUBLISHER]

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates. [ABSTRACT FROM PUBLISHER]

Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital converters (ADCs) are two low power solutions for wireless systems with large antenna arrays, such as millimeter wave and massive multiple-input multiple-output systems. Most prior work represents two extreme cases in which either a small number of radio frequency (RF) chains with full-resolution ADCs, or low-resolution ADC with a number of RF chains equal to the number of antennas is assumed. In this paper, a generalized hybrid architecture with a small number of RF chains and a finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and singular value decomposition-based transmission methods are derived. Results show that the achievable rate is comparable to that obtained by full-precision ADC receivers at low and medium SNRs. A trade-off between the achievable rate and power consumption for the different numbers of bits and RF chains is devised. This enables us to draw some conclusions on the number of ADC bits needed to maximize the system energy efficiency. Numerical simulations show that coarse ADC quantization is optimal under various system configurations. This means that hybrid combining with coarse quantization achieves better energy-rate trade-off compared with both hybrid combining with full-resolutions ADCs and 1-bit ADC combining. [ABSTRACT FROM PUBLISHER]

We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research. [ABSTRACT FROM PUBLISHER]

Sharing the spectrum among multiple operators seems promising in millimeter wave (mmWave) systems. One explanation is the highly directional transmission in mmWave, which reduces the interference caused by one network on the other networks sharing the same resources. In this paper, we model a mmWave cellular system, where an operator that primarily owns an exclusive-use license of a certain band can sell a restricted secondary license of the same band to another operator. This secondary network has a restriction on the maximum interference it can cause to the original network. Using stochastic geometry, we derive expressions for the coverage and the rate of both networks, and establish the feasibility of secondary licensing in licensed mmWave bands. To explain economic tradeoffs, we consider a revenue-pricing model for both operators in the presence of a central licensing authority. Our results show that the original operator and central network authority can benefit from secondary licensing when the maximum interference threshold is properly adjusted. This means that the original operator and central licensing authority have an incentive to permit a secondary network to restrictively share the spectrum. Our results also illustrate that the spectrum sharing gains increase with narrow beams and when the network densifies. [ABSTRACT FROM PUBLISHER]

The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions. [ABSTRACT FROM PUBLISHER]

Hybrid analog/digital precoding offers a compromise between hardware complexity and system performance in millimeter wave (mmWave) systems. This type of precoding allows mmWave systems to leverage large antenna array gains that are necessary for sufficient link margin, while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrow-band mmWave systems, with perfect or estimated channel knowledge at the transmitter. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers wideband mmWave systems with a limited feedback channel between the transmitter and receiver. First, the optimal hybrid precoding design for a given RF codebook is derived. This provides a benchmark for any other heuristic algorithm and gives useful insights into codebook designs. Second, efficient hybrid analog/digital codebooks are developed for spatial multiplexing in wideband mmWave systems. Finally, a low-complexity yet near-optimal greedy frequency selective hybrid precoding algorithm is proposed based on Gram–Schmidt orthogonalization. Simulation results show that the developed hybrid codebooks and precoder designs achieve very-good performance compared with the unconstrained solutions while requiring much less complexity. [ABSTRACT FROM PUBLISHER]

Background: Alzheimer's disease is associated with amyloid-beta (Aβ)-induced microglia activation. This pro-inflammatory response promotes neuronal damage, and therapies are sought to limit microglial activation. Screening efforts to develop new pharmacological inhibitors require a robust in vitro cell system. Current models lack significant responses to Aβ, and their use in examining age-related neurodegenerative diseases is questionable. For example, the commonly used BV-2 microglial line was derived from embryonic mononuclear cells and its activation by various stimuli is limited. To this end, we have established a new immortalized microglial (IMG) cell line from adult murine brain. The objective of this study was to characterize Aβ-induced activation of IMG cells, and here, we demonstrate the ability of cannabinoids to significantly reduce this inflammatory response.Methods: Microglial cells derived from adult murine brain were immortalized via infection with the v-raf/v-myc retrovirus under conditions that selectively promote microglia growth. The presence or absence of markers CD11b and F4/80 (microglial), NeuN (neuronal), and GFAP (astrocytic) was assessed by immunofluorescence microscopy and western blotting. Using IMG and BV-2 cells, levels of pro- and anti-inflammatory transcripts in response to extracellular stimuli were determined by quantitative PCR (qPCR). Phagocytosis of fluorescent beads and fluorescein isothiocyanate (FITC)-labeled Aβ oligomers was assessed using flow cytometry and fluorescence microscopy. FITC-Aβ uptake was quantified using a fluorescence plate reader. The ability of cannabinoids to mitigate Aβ-induced expression of inducible nitric oxide synthase (iNOS) was evaluated.Results: IMG cells express the microglial markers CD11b and F4/80 but not NeuN or GFAP. Relative to BV-2 cells, IMG cells increased iNOS (>200-fold) and Arg-1 (>100-fold) in response to pro- and anti-inflammatory stimuli. IMG cells phagocytose foreign particles and Aβ oligomers, with the latter trafficked to phagolysosomes. Aβ-induced activation of IMG cells was suppressed by delta-9-tetrahydrocannabinol and the CB2-selective agonist JWH-015 in a time- and concentration-dependent manner.Conclusions: IMG cells recapitulate key features of microglial cell activation. As an example of their potential pharmacological use, cannabinoids were shown to reduce activation of Aβ-induced iNOS gene expression. IMG cells hold promising potential for drug screening, mechanistic studies, and functional investigations directed towards understanding how Aβ interacts with microglia. [ABSTRACT FROM AUTHOR]

Antenna arrays will be an important ingredient in millimeter-wave (mmWave) cellular systems. A natural application of antenna arrays is simultaneous transmission to multiple users. Unfortunately, the hardware constraints in mmWave systems make it difficult to apply conventional lower frequency multiuser MIMO precoding techniques at mmWave. This paper develops low-complexity hybrid analog/digital precoding for downlink multiuser mmWave systems. Hybrid precoding involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware. The proposed algorithm configures hybrid precoders at the transmitter and analog combiners at multiple receivers with a small training and feedback overhead. The performance of the proposed algorithm is analyzed in the large dimensional regime and in single-path channels. When the analog and digital precoding vectors are selected from quantized codebooks, the rate loss due to the joint quantization is characterized, and insights are given into the performance of hybrid precoding compared with analog-only beamforming solutions. Analytical and simulation results show that the proposed techniques offer higher sum rates compared with analog-only beamforming solutions, and approach the performance of the unconstrained digital beamforming with relatively small codebooks. [ABSTRACT FROM PUBLISHER]

Previous studies have shown that administration of ferristatin II to rats is associated with decreased serum iron, reduced transferrin saturation, and increased hepatic hepcidin expression. BMP and IL-6 signaling act via Smad and Stat3 transcription factors, respectively, to increase expression of hepcidin, the master regulator of iron metabolism. In this study, we aimed to explore the underlying mechanism of ferristatin II action on hepcidin production. We found that ferristatin II greatly increased hepcidin expression both in vivo and in vitro. In the rat liver, ferristatin II treatment decreased expression of Smad downstream targets Smad7 and Id1 and increased expression of Stat3 downstream targets α-2-macroglobulin, α-1-acid glycoprotein, and C-reactive peptide. Ferristatin II also increased Stat3 phosphorylation in the rat liver without affecting serum or hepatic IL-6 levels. It is unclear whether the Stat3 activation observed in vivo is a cause or a consequence to hepcidin induction. Reporter gene expression studies demonstrated that ferristatin II synergized with BMP6 and IL-6 to enhance hepcidin expression in vitro. However, this synergy was not due to activation of either Smad or Stat3 signaling, raising the possibility that ferristatin II may activate a novel pathway for hepcidin regulation. [ABSTRACT FROM AUTHOR]

Millimeter-wave communication is one way to alleviate the spectrum gridlock at lower frequencies while simultaneously providing high-bandwidth communication channels. MmWave makes use of MIMO through large antenna arrays at both the base station and the mobile station to provide sufficient received signal power. This article explains how beamforming and precoding are different in MIMO mmWave systems than in their lower-frequency counterparts, due to different hardware constraints and channel characteristics. Two potential architectures are reviewed: hybrid analog/digital precoding/combining and combining with low-resolution analog- to-digital converters. The potential gains and design challenges for these strategies are discussed, and future research directions are highlighted. [ABSTRACT FROM PUBLISHER]

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained. The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Using the estimated channel, this paper proposes a new hybrid analog/digital precoding algorithm that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference. [ABSTRACT FROM PUBLISHER]

The millimeter-wave (mmWave) band offers the potential for high-bandwidth communication channels in cellular networks. It is not clear, however, whether both high data rates and coverage in terms of signal-to-noise-plus-interference ratio can be achieved in interferencelimited mmWave cellular networks due to the differences in propagation conditions and antenna topologies. This article shows that dense mmWave networks can achieve both higher data rates and comparable coverage relative to conventional microwave networks. Sum rate gains can be achieved using more advanced beamforming techniques that allow multiuser transmission. The insights are derived using a new theoretical network model that incorporates key characteristics of mmWave networks. [ABSTRACT FROM PUBLISHER]

Tumor-associated macrophages play a critical role in breast tumor progression; however, it is still unclear what effector molecular mechanisms they employ to impact tumorigenesis. Ferritin is the primary intracellular iron storage protein and is also abundant in circulation. In breast cancer patients, ferritin is detected at higher levels in both serum and tumor lysates, and its increase correlates with poor clinical outcome. In this study, we comprehensively examined the distribution of ferritin in normal and malignant breast tissue at different stages in tumor development. Decreased ferritin expression in cancer cells but increased infiltration of ferritin-rich CD68-positive macrophages was observed with increased tumor histological grade. Interestingly, ferritin stained within the stroma surrounding tumors suggesting local release within the breast. In cell culture, macrophages, but not breast cancer cells, were capable of ferritin secretion, and this secretion was further increased in response to pro-inflammatory cytokines. We next examined the possible functional significance of extracellular ferritin in a breast cancer cell culture model. Ferritin stimulated the proliferation of the epithelial breast cancer cell lines MCF7 and T47D. Moreover, this proliferative effect was independent of the iron content of ferritin and did not increase intracellular iron levels in cancer cells indicating a novel iron-independent function for this protein. Together, these findings suggest that the release of ferritin by infiltrating macrophages in breast tumors may represent an inflammatory effector mechanism by which ferritin directly stimulates tumorigenesis. [ABSTRACT FROM AUTHOR]

Approximately half of all HER2/neu-overexpressing breast cancer patients do not respond to trastuzumab-containing therapy. Therefore, there remains an urgent and unmet clinical need for the development of predictive biomarkers for trastuzumab response. Recently, several lines of evidence have demonstrated that the inflammatory tumor microenvironment is a major contributor to therapy resistance in breast cancer. In order to explore the predictive value of inflammation in breast cancer patients, we measured the inflammatory biomarkers serum ferritin and C-reactive protein (CRP) in 66 patients immediately before undergoing trastuzumab-containing therapy and evaluated their progression-free and overall survival. The elevation in pre-treatment serum ferritin (>250 ng/ml) or CRP (>7.25 mg/l) was a significant predictor of reduced progression-free survival and shorter overall survival. When patients were stratified based on their serum ferritin and CRP levels, patients with elevation in both inflammatory biomarkers had a markedly poorer response to trastuzumab-containing therapy. Therefore, the elevation in inflammatory serum biomarkers may reflect a pathological state that decreases the clinical efficacy of this therapy. Anti-inflammatory drugs and life-style changes to decrease inflammation in cancer patients should be explored as possible strategies to sensitize patients to anti-cancer therapeutics. [ABSTRACT FROM AUTHOR]

In recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study. [ABSTRACT FROM AUTHOR]

Extensive desmoplasia is a hallmark of pancreatic ductal adenocarcinoma (PDAC), which frequently associates with treatment resistance. Recent findings indicate that a combination of photodynamic therapy and the multi-kinase inhibitor cabozantinib achieved local tumor control and a significant decrease in tumor metastases in preclinical PDAC models, but the underlying therapeutic mechanisms remain unclear. This study elucidates the molecular basis of this multi-agent regimen, focusing on the role of MET signaling. Since MET activation stems from its interaction with hepatocyte growth factor (HGF), which is typically secreted by fibroblasts, we developed heterotypic PDAC microtumor models that recapitulate these interactions. In these models, MET signaling can be constitutively activated through paracrine and autocrine mechanisms. Photodynamic therapy caused significant elevations in HGF secretion by fibroblasts, suggesting it plays a complex role in the modulation of the paracrine HGF–MET signaling cascade in desmoplastic tumors. Blocking MET phosphorylation with adjuvant cabozantinib caused a significant improvement in photodynamic therapy efficacy, most notably by elevating spheroid necrosis at low radiant exposures. These findings highlight that adjuvant photodynamic therapy can augment chemotherapy efficacies, and potentially achieve improved management of desmoplastic PDAC in a more tolerable manner. [ABSTRACT FROM AUTHOR]