Features of smaller ribosomes in Candidate Phyla Radiation (CPR) bacteria revealed with a molecular evolutionary analysis

The Candidate Phyla Radiation (CPR) is a large bacterial group consisting mainly of uncultured lineages. They have small cells and small genomes, and often lack ribosomal proteins L1, L9, and/or L30, which are basically ubiquitous in ordinary (non-CPR) bacteria. Here, we comprehensively analyzed the genomic information of CPR bacteria and identified their unique properties. In the distribution of protein lengths in CPR bacteria, the peak was at around 100–150 amino acids, whereas the position of the peak varies in the range of 100–300 amino acids in free-living non-CPR bacteria, and at around 100–200 amino acids in most symbiotic non-CPR bacteria. These results show that CPR bacteria have smaller proteins on average, like symbiotic non-CPR bacteria. We found that ribosomal proteins L28, L29, L32, and L33 are also deleted in CPR bacteria, in a lineage-specific manner. Moreover, the sequences of approximately half of all ribosomal proteins in CPR differ, in part, from those of non-CPR bacteria, with missing regions or specific added region. We also found that several regions of the 16S, 23S, and 5S rRNAs are lacking in CPR bacteria and that the total predicted length of the three rRNAs in CPR bacteria is smaller than that in non-CPR bacteria. The regions missing in the CPR ribosomal proteins and rRNAs are located near the surface of the ribosome, and some are close to one another. These observations suggest that ribosomes are smaller in CPR bacteria than in free-living non-CPR bacteria, with simplified surface structures.

An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks

The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.

Morphometric characterization and classification of vicuña sperm (Vicugna vicugna) using ISAS® CASA-Morph system

The aim of the study was to determine the morphological and morphometric characteristics of the vicuña sperm (Vicugna vicugna), looking for possible subpopulations in its structure. Semen was collected by electroejaculation method from three adult male vicuñas weighing 50.33 ± 2.52 kg, once per animal. The volume, color and concentration, sperm morphology and morphometry were determined. The samples for the analysis of sperm morphology and morphometry were stained with Hemacolor®, and analyzed using the CASA-Morph, Integrated Semen Analysis System (ISAS®v1). Five forms of the vicuña sperm head were distinguished: normal, piriform, long, short and rounded. The morphometric parameters determined were the length, width, area, perimeter, ellipticity, elongation, regularity and rugosity of the vicuña sperm head, percentage of acrosome, head gray level, as well as the width, area, distance and angle of insertion of midpiece of the sperm. In relation to morphology, significant differences (P <0.05) were found in the percentage distribution of head shapes, with the normal shape (55.7%) being the majority and different from the other shapes. Significant differences (P <0.05) were found between animals in the morphometric variables of head, percentage of acrosome, ellipticity, rugosity, elongation and gray level; while the variables of the midpiece and regularity did not show differences (P> 0.05). The morphometric variables were distributed in four main components (PCA) called elongation, area, circularity and midpiece width, which explained 84.59% of the total variance. The cluster analysis determined five subpopulations (SP): SP1 grouped small cells of low length, width and area (18.8%); SP2 of sperm of large size, both in area and width of the head (17.38%); SP3 of rounded cells with high values of percentage of acrosome and head gray level (24.04%); SP4 of spermatozoa of intermediate size and elongated, with greater ellipticities and elongation (23.61%); and SP5 of cells of intermediate size and short, with low values of area and length (16.71%).

Interference Suppression in Heterogeneous Massive MIMO Systems with Imperfect CSI

Interference management is of paramount importance in heterogeneous massive mimo networks (HetNet). In this paper, an algorithm has been suggested to suppress the interference in large-MIMO HetNets with imperfect channel state information(CSI). The proposed technique controls both the intra-tier and cross-tier interference of the macrocell as well as the small cells. The intra-tier interference of the macrocell as well as the cross-tier interference have been minimized under maximum transmission power and minimum signal to interference and noise ratio (SINR) constraint. The channel estimation error matrix has also been modeled using the joint sparsity property. The precoding algorithm is thus achieved through the application of semi-definite relaxation and block coordinate descent techniques. The intra-tier interference of the small cells are addressed with the aid of the zero forcing scheme. The proposed method has been validated through various simulations which confirm the superiority of the algorithm over its counterparts.

Prothrombotic Phenotype in COVID-19: Focus on Platelets

COVID-19 infection is associated with a broad spectrum of presentations, but alveolar capillary microthrombi have been described as a common finding in COVID-19 patients, appearing as a consequence of a severe endothelial injury with endothelial cell membrane disruption. These observations clearly point to the identification of a COVID-19-associated coagulopathy, which may contribute to thrombosis, multi-organ damage, and cause of severity and fatality. One significant finding that emerges in prothrombotic abnormalities observed in COVID-19 patients is that the coagulation alterations are mainly mediated by the activation of platelets and intrinsically related to viral-mediated endothelial inflammation. Beyond the well-known role in hemostasis, the ability of platelets to also release various potent cytokines and chemokines has elevated these small cells from simple cell fragments to crucial modulators in the blood, including their inflammatory functions, that have a large influence on the immune response during infectious disease. Indeed, platelets are involved in the pathogenesis of acute lung injury also by promoting NET formation and affecting vascular permeability. Specifically, the deposition by activated platelets of the chemokine platelet factor 4 at sites of inflammation promotes adhesion of neutrophils on endothelial cells and thrombogenesis, and it seems deeply involved in the phenomenon of vaccine-induced thrombocytopenia and thrombosis. Importantly, the hyperactivated platelet phenotype along with evidence of cytokine storm, high levels of P-selectin, D-dimer, and, on the other hand, decreased levels of fibrinogen, von Willebrand factor, and thrombocytopenia may be considered suitable biomarkers that distinguish the late stage of COVID-19 progression in critically ill patients.

Biophysical controls on seasonal changes in the structure, growth, and grazing of the size-fractionated phytoplankton community in the northern South China Sea

The size-fractionated phytoplankton growth and microzooplankton grazing are crucial for the temporal change of community size structure, regulating not only trophic transfer but also the carbon cycle of the ocean. However, the size-dependent growth and grazing dynamics on a monthly or an annual basis are less addressed in the coastal ocean. In this paper, the seasonal responses of the size-fractionated phytoplankton growth and grazing to environmental change were examined over 1 year at a coastal site of the northern South China Sea. We found a nanophytoplankton-dominated community with strong seasonal variations in all size classes. Phytoplankton community growth rate was positively correlated to nutrients, with community grazing rate correlating to the total chlorophyll a at the station, reflecting a combined bottom-up and topdown effect on phytoplankton population dynamics. Further analyses suggested that the specific growth rate of microphytoplankton was significantly influenced by phosphate, and that of nanophytoplankton was influenced by light, although picophytoplankton growth was controlled by both nitrate and temperature. In addition, the specific grazing rate of nanophytoplankton was well correlated to phytoplankton standing stock, while that of micro- and pico-compartments was negatively influenced by ciliate abundance and salinity. Finally, a lower grazing impact for micro-cells (38 %) than nano- and pico-cells (72 % and 60 %, respectively) may support size-selective grazing of microzooplankton on small cells at this eutrophic system.

Reinforcement Learning for Interference Coordination Stackelberg Games in Heterogeneous Cellular Networks

In future heterogeneous cellular networks with small cells, such as D2D and relay, interference coordination between macro cells and small cells should be addressed through effective resource allocation and power control. The two-step Stackelberg game is a widely used and feasible model for resource allocation and power control problem formulation. Both in the follower games for small cells and in the leader games for the macro cell, the cost parameters are a critical variable for the performance of Stackelberg game. Previous studies have failed to adequately address the optimization of cost parameters. This paper presents a reinforcement learning approach for effectively training cost parameters for better system performance. Furthermore, a two-stage pretraining plus ε -greedy algorithm is proposed to accelerate the convergence of reinforcement learning. The simulation results can demonstrate that compared with the three beachmarking algorithms, the proposed algorithm can enhance average throughput of all users and cellular users by up to 7% and 9.7%, respectively.

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

This paper presents an experimental realization of multiband 5G new radio (NR) optical front haul (OFH) based radio over fiber (RoF) system using digital predistortion (DPD). A novel magnitude-selective affine (MSA) based DPD method is proposed for the complexity reduction and performance enhancement of RoF link followed by its comparison with the canonical piece wise linearization (CPWL), decomposed vector rotation method (DVR) and generalized memory polynomial (GMP) methods. Similarly, a detailed study is shown followed by the implementation proposal of novel neural network (NN) for DPD followed by its comparison with MSA, CPWL, DVR and GMP methods. In the experimental testbed, 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible-waveform signal at 3 GHz with 20 MHz bandwidth is used to cover enhanced mobile broad band and small cells scenarios. A dual drive Mach Zehnder Modulator having two distinct radio frequency signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The experimental results are presented in terms of adjacent channel power ratio (ACPR), error vector magnitude (EVM), number of estimated coefficients and multiplications. The study aims to identify those novel methods such as MSA DPD are a good candidate to deploy in real time scenarios for DPD in comparison to NN based DPD which have a slightly better performance as compared to the proposed MSA method but has a higher complexity levels. Both, proposed methods, MSA and NN are meeting the 3GPP Release 17 requirements.

A free-geometry geodynamic modelling of surface gravity changes using Growth-dg software

Globally there is abundant terrestrial surface gravity data used to study the time variation of gravity related to subsurface mass and density changes in different geological, geodynamical and geotechnical environments. We present here a tool for analysing existing and newly acquired, 4D gravity data, which creates new findings from its reuse. Our method calculates in an almost automatic way the possible sources of density change responsible for the observed gravity variations. The specifics of the new methodology are: use of a low number of observation points, relatively small source structures, low signal/noise ratio in the data, and a free 3D source geometry without initial hypothesis. The process is based on the non-linear adjustment of structures defined by aggregation of small cells corresponding to a 3D section of the sub-floor volume. This methodology is implemented in a software tool, named GROWTH-dg, which can be freely downloaded for immediate use, together with a user manual and application examples.