Search

Background: One major limitation for broader applicability of allogeneic hematopoietic cell transplantation (allo-HCT) in the past was the lack of HLA-matched histocompatible donors. Preclinical mouse studies using T-cell depleted haploidentical grafts led to an increased interest in the use of ex vivo T-cell depleted (TCD) haploidentical allo-HCT. TCD grafts through negative (T-cell depletion) or positive (CD34+ cell selection) techniques have been investigated to reduce the risk of graft-versus-host disease (GVHD) given the known implications of alloreactive T cells. A more practical approach to deplete alloreactive T cells in vivo using high doses of cyclophosphamide after allografting has proved to be feasible in overcoming the HLA barrier. Such approach has extended allo-HCT feasibility to patients for whom donors could not be found in the past. Nowadays, haploidentical donors represent a common donor source for patients in need of an allo-HCT. The broad application of haploidentical donors became possible by understanding the importance of depleting alloreactive donor T cells to facilitate engraftment and reduce incidence and severity of GVHD. These techniques involve ex vivo graft manipulation or in vivo utilization of pharmacologic agents, notably post-transplant cyclophosphamide (PTCy). Discussion: While acknowledging that no randomized controlled prospective studies have been yet conducted comparing TCD versus PTCy in haploidentical allo-HCT recipients, there are two advantages that would favor the PTCy, namely ease of application and lower cost. However, emerging data on adverse events associated with PTCy including, but not limited to cardiac associated toxicities or increased incidence of post-allograft infections, and others, are important to recognize. [ABSTRACT FROM AUTHOR]

Cell competition arises in heterogeneous tissues when neighbouring cells sense their relative fitness and undergo selection. It has been a challenge to define contexts in which cell competition is a physiologically relevant phenomenon and to understand the cellular features that underlie fitness and fitness sensing. Drawing on examples across a range of contexts and length scales, we illuminate molecular and cellular features that could underlie fitness in diverse tissue types and processes to promote and reinforce longterm maintenance of tissue function. We propose that by broadening the scope of how fitness is defined and the circumstances in which cell competition can occur, the field can unlock the potential of cell competition as a lens through which heterogeneity and its role in the fundamental principles of complex tissue organisation can be understood. [ABSTRACT FROM AUTHOR]

Abstract This work discusses a novel approach to a simulation-based cell selection that does not depend on static counts of cells or simplified models used in the cell selection process. The proposed method of cell selection uses advanced simulation to analyse the actual conditions and dynamically select the optimal cell to better suit the real conditions of the network and preference of the client. This research makes important theoretical and practical contributions that increase any simulation’s performance and reliability aspects. The simulations conducted in the study demonstrate several major advantages. The method results in substantially lower call drops and higher throughputs of data. The outcome of the study is an adaptive and inherently interactive method that can be used in a plurality of networking circumstances, from 4 to 5G networks. The work more than adequately addresses the issue of cell selection and connecting, and improves the reliability and efficiency of any network that uses this method. This product can be easily implemented and can be used by network developers to ameliorate their networks’ performance. The work’s main theoretical contribution is the fact that optimal methods are made that undoubtedly make a simulation a more efficient phenomenon and that the components would interact more effectively together.

This work discusses a novel approach to a simulation-based cell selection that does not depend on static counts of cells or simplified models used in the cell selection process. The proposed method of cell selection uses advanced simulation to analyse the actual conditions and dynamically select the optimal cell to better suit the real conditions of the network and preference of the client. This research makes important theoretical and practical contributions that increase any simulation's performance and reliability aspects. The simulations conducted in the study demonstrate several major advantages. The method results in substantially lower call drops and higher throughputs of data. The outcome of the study is an adaptive and inherently interactive method that can be used in a plurality of networking circumstances, from 4 to 5G networks. The work more than adequately addresses the issue of cell selection and connecting, and improves the reliability and efficiency of any network that uses this method. This product can be easily implemented and can be used by network developers to ameliorate their networks' performance. The work's main theoretical contribution is the fact that optimal methods are made that undoubtedly make a simulation a more efficient phenomenon and that the components would interact more effectively together. Article Highlights: Dynamic cell selection improves performance, reducing call drops and enhancing throughput. Simulation framework adapts to real-time conditions, offering flexibility for 4G and 5G networks. Scalable method provides valuable insights for optimizing cellular network efficiency and QoS. [ABSTRACT FROM AUTHOR]

Coppe and zinc are priority pollutants in city soils. Copper and zinc are the limiting factors for lawn grasses. Cell selection can increase the resistance of lawn grasses to zinc and copper. The effect of zinc on the morphogenic ability of the callus was determined. The results of this study showed that zinc is less toxic to calli than copper. The method of obtaining lawn grass resistant to zinc has been developed. The results were used to develop the cell selection technology for obtaining plants resistant to the complex effect of zinc and copper. Concentrations of Copper (75 mg/l) and zinc (150 mg/L) were selected as selective. The author developed the cell selection scheme for obtaining plants resistant to the complex effect of Cu and Zn. The regenerants showed increased resistance to copper and zinc. [ABSTRACT FROM AUTHOR]

This paper proposes a supervised learning based cell selection/reselection prediction technique incorporating ensembles, and is reinforced by readily available lasso and ridge regression for regularization. This data-driven approach replaces event-driven cell selection/reselection requests and helps in solving complex calculations for generalizing the loss function. The proposed work uses the term “cell selection for affluence'' to refer to both cell selection and reselection. In technical terms, handovers are reserved for calls, and cell selections are for data services. This study highlights that by exploiting advance information about the success or failure of cell selection request, how a data-driven cell selection can improve the cell selection success rate of future mobile networks. Our results demonstrate, the proposed algorithm can improve cell selection success rate by up to 4% and quickens model response time by up to 602 ms.

The growing world population, public awareness of animal welfare, environmental impacts and changes in meat consumption leads to the search for novel approaches to food production. Novel foods include products with a new or specifically modified molecular structure, foods made from microorganisms, fungi, algae or insects, as well as from animal cell or tissue cultures. The latter approach is known by various names: "clean meat", "in vitro meat" and "cell‐cultured" or "(cell‐)cultivated meat". Here, cells isolated from agronomically important species are expanded ex vivo to produce cell biomass used in unstructured meat or to grow and differentiate cells on scaffolds to produce structured meat analogues. Despite the fast‐growing field and high financial interest from investors and governments, cultivated meat production still faces challenges ranging from cell source choice, affordable expansion, use of cruelty‐free and food‐grade media, regulatory issues and consumer acceptance. This overview discusses the above challenges and possible solutions and strategies in the production of cultivated meat. The review integrates multifaceted historical, social, and technological insights of the field, and provides both an engaging comprehensive introduction for general interested and a robust perspective for experts. [ABSTRACT FROM AUTHOR]

Aim. To characterize a new hormone-independent strain 1-27 of R. serpentina tissue culture. Methods. Plant tissue culture, biochemical and cytological analysis, statistical methods. Results. The strain is maintained on the specially designed simple-composition hormone-free 10C medium. The maximum biomass yield was at day 69 of subculture (693.5 g/l of live biomass, 43.0 g/l of dry biomass), the weight of cell biomass increased 15-18 times over a subculture period. The maximum content of indole alkaloids was observed from 88 to 108 days of subculture. The dry biomass contained 4.0% of total indole alkaloids, 1.64% of ajmaline-like alkaloids, 0.789% of ajmaline, 0.337% of vomilenine, and 0.006% of each yohimbine and reserpine. The strain is a heterogeneous mixoploid cell population with a modal class of 22 to 33 chromosomes (2n = 22), which characterized by significant cell and nuclear polymorphism. Conclusions. The K-27M strain of R. serpentina cultured tissue is a prospective producer of indole alkaloids. [ABSTRACT FROM AUTHOR]

A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes. [ABSTRACT FROM AUTHOR]

Environmental pollution with heavy metals has a negative impact on lawn grasses. Heavy metals are one of the priority pollutants of anthropogenic ecosystems. Earlier, plants Agrostis stolonifera, resistant to cadmium, were obtained using biotechnological method. Plants that are resistant to one heavy metal may be cross-resistant to another. The assessment of the resistance of plants obtained by biotechnological methods to other heavy metals is of practical value. The object of our study was to lawn grass - Agrostis stolonifera L. The aim of this work was to assess the tolerance of the next generation descendants of the regenerant Agrostis stolonifera, resistant to cadmium, to one of the most phytotoxic heavy metals - copper. Cadmium -tolerant plants were more resistant to copper. The tolerance of cadmium – resistant plants to copper is associated with nonspecific mechanisms. However, the increase in plant resistance was not very significant. Therefore, it is more expedient to obtain plants that are resistant to copper. [ABSTRACT FROM AUTHOR]

The 5th generation of mobile communications, currently being rolled out, aims to improve network performance and efficiency over the older generations. With mmWaves, densified cell deployment is necessary for 5G networks, increasing capacity and improving network coverage. This imposes a considerable increase in the energy consumption of the 5G stations, which not only increases operating expenses for operators but also burdens the environment. Optimizing the energy consumption of 5G networks would be necessary to curb the energy curve. In this context, this paper presents a new algorithm called Energy Consumption Optimization Algorithm (ECOA), which combines cell selection and standby techniques to optimize energy consumption while preserving network performance. A comparison is conducted between ECOA and standard cell selection modes to evaluate the performance of the conventional approach. Our algorithm exhibits good performance, particularly in high-density, high-load scenarios. For instance, in a site with 25 Pico base stations serving 500 users, our algorithm achieves an average throughput of 23 Mb/s per user while consuming 1750.75 W of energy. This represents a 2.44% increase in energy consumption compared to the optimal solution.

Lead is one of the priority soil pollutants among heavy metals. To increase the species diversity of ecosystems, it is necessary to increase the resistance of plants to lead. The aim of the work was to obtain plants resistant to lead. The objects of our study were to lawn grasses. The effect of lead on the growth and regenerative ability of calli was determined. The results of this work showed that lead is less toxic to calli than copper. Biotechnological method for obtaining lead resistant plants has been developed. The effect of lead on the growth of regenerants and original plants was determined. Agrostis stolonifera plants that are obtained after cell selection have demonstrated a high degree of resistance to lead. Can the developed technology be used for other lawn grasses? We obtained lead resistant plants Festuca rubra. Therefore, using cell selection can increase the tolerance of lawn grasses to lead. [ABSTRACT FROM AUTHOR]

The deployment of cost-effective small cells to create ultra-high-density (UDN) heterogeneous networks in 5 G networks has emerged as a potentially effective strategy for enhancing network coverage and optimising resource allocation. However, UDN makes network selection more challenging due to the densification of small cells in 5 G and their heterogeneity. This research presents an efficient small cell selection handover mechanism for 5 G V2X networks. The proposed mechanism uses a Multiple Criteria Decision Making (MCDM) technique for the handover best cell selection to improve the overall performance. The proposed handover mechanism is context sensitive and it adapts to changing network conditions, ensuring efficient handovers during high-speed vehicular movement. Furthermore, the mechanism incorporates the concept of small cell Stay Time, which may reduce unnecessary handovers. The simulation results reveal that the proposed mechanism outperforms traditional handover techniques and Handover Decision-making Algorithm (HDMA) mechanisms significantly in terms of reducing the number of frequent handovers, minimizing link failures, and minimizing ping-pong with an average of 66 % reduction for unnecessary handovers. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the sugar beet genotypes for resistance to hyperthermia and obtain temperature-resistant lines. Nine hybrids and one variety of sugar beet were screened. Cotyledonary leaves and hypocotyls isolated from aseptic seedlings were used for induction of callus and subsequent subcultivation. To create hyperthermic conditions, the callus lines were maintained in thermostats at temperatures of +27 °C, +41 °C, +45 °C, and +47 °C. The effect of high temperatures on the callus tissue was assessed by the specific callus diameter index. The free proline was detected with chromatography. As result of callus tissue exposed to temperatures of +41 °C, 45 °C and 47 °C, on the 9th day of cultivation at high temperatures, significant differences were observed in the size and colouration of the callus tissues. At a moderate temperature (+41 °C), the growth of the callus mass was somewhat higher compared to the control. At a high temperature (+45 °C), the intensity of the growth processes decreased and ceased at a temperature of +47 °C. After transfer and subsequent cultivation of callus tissues in regeneration medium MSR -- Murashige and Skoog medium for regeneration, all genotypes demonstrated the formation of morphological structures that initiated the formation of regenerated plants. The number of regenerated plants largely fluctuated over temperatures and almost was not related to genotypes. Consequently, the temperature-resistant lines obtained as a result of extreme heat treatment, differ in terms of the specific diameter of callus. [ABSTRACT FROM AUTHOR]

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low-power wide-area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this article, we make the first attempt to examine and optimize the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve five typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find four potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE. [ABSTRACT FROM AUTHOR]

Cellular operators deploy 4G License Assisted Access (LAA) and 5G NR-U base stations in the unlicensed spectrum to enhance overall network capacity. This work highlights a unique phenomenon related to Physical Cell Id (PCI) that is observed in public LAA operator deployments. Notably, the licensed and unlicensed carriers of a device may have the same PCI or different PCIs. The phenomenon is triggered by the combined effect of unlicensed deployment architectures and cell selection mechanisms. Consequently, the phenomenon will intensify in the 5G NR-U, whose public deployment will soon begin. Unfortunately, the impact of this phenomenon on coexistence network performance is unexplored. It is also desirable to accurately identify the PCI scenarios at the device for improved cell selection and network performance. However, the data imbalance makes the classification problem challenging. This work addresses these problems through the following approach. Operator data from three LAA cellular providers is gathered and analyzed using machine learning algorithms. The impact of the phenomenon on LTE, LAA, and Wi-Fi components is demonstrated in three steps: First, the variation in network performance prediction accuracy in the PCI scenarios is examined. Second, the efficacy of numerosity reduction techniques used in data-driven cell selection is evaluated in both PCI scenarios. The third step entails a comparison of operator data analysis with network measurements. On-site experiments are conducted at the same PCI and different PCI sites to study differences in real-time network performance. A controlled LTE-WiFi coexistence environment is created and multiple traffic categories are considered. Finally, a class-weight-based solution is proposed for PCI scenario identification. F-score of 0.75 and AUC-ROC of 0.84 is achieved for LAA, with a minimalist feature set consisting of SINR and Throughput.

Background. An effective way to obtain barley (Hordeum vulgare L.) genotypes stress-tolerant to cadmium, with a low level of toxic ion accumulation in grain, is the selection of cells in selective in vitro systems, based on somaclonal variability, which promotes the formation of specific and nonspecific adaptive mechanisms.Materials and methods. The object of the study was cv. ‘999-93’, developed by selection from a hybrid combination of spring barley (Luly × Conrad) × 2867-80, and its regenerated forms in seed reproductions of the 3–5th generation, induced in the process of cell selection on media with cadmium, aluminum and polyethylene glycol. The plants were grown under normal soil conditions and against a provocative background for cadmium.Results. The contribution of the environment-forming activity in the roots of the studied genotypes to inactivation of toxic ions appeared insignificant. The total removal of cadmium by plants against a provocative background increased 22.5 times, reaching 5.8–10.3 mg/kg of dry phytomass when distributed among organs: roots (91.9–93.4%) > stems (5.9–7.8%) > grain (0.5–0.8%). The amount of toxic metal in grain increased 11 times in the original form and 2.8–6.8 times in regenerants. The negative effect of excess cadmium in the soil on the accumulation of zinc in barley was shown. There was no gradation in the importance of organs for zinc accumulation or any presence of functional barriers preventing this. Regenerants induced on selective media with cadmium had the greatest adaptive advantages to stress: pronounced barrier functions of roots, minimal accumulation of toxic ions in aerial organs, and high seed productivity (they exceeded the original genotype by 35.5%). Adaptive reactions associated with the limitation of cadmium accumulation in plant tissues of regenerants, obtained by in vitro selections with aluminum and an osmotic, were shown to be weak.

The studies were carried out on the basis of the Rudnitsky Federal Agrarian Scientific Center of the Northeast (Kirov). The breeding potential of a new barley variety of regenerated origin, Dialog (550-08), created on the basis of a hybrid combination of varieties Valeta (k-22345, the Netherlands) and Lulu (k-25169, Germany) was evaluated. Barley was introduced into in vitro culture, callus was induced, and callus lines were sequentially selected on acidic (pH 3.8) selective media with aluminum ions, followed by plant regeneration. The seed progeny of regenerated plants served as the starting material for further cell selection. Regenerative lines, which were studied in laboratory, vegetation, and field experiments, were obtained as a result of a three-stage cell selection against the background of an increasing concentration gradient of aluminum ions (20 and 40 mg/L) with the introduction of 1.0 mg/L abscisic acid as an immunomodulator. A comparative analysis was carried out with respect to the standards (Bios 1 and Belgorodsky 100) and the aluminum tolerant variety Novichok. The regenerative lines had an advantage under alumina stress. In water culture, their root length index was equal to 1.17, while it was 0.80 for the original form and 0.84 for the standard. The yield advantage of the regenerated line over the standard was 0.66 t/ha (90.4%) in vegetation experiments and 0.85 t/ha (97.6%) in field trials. A variety of barley of regenerative origin has been created; it is characterized by high adaptive properties in the conditions of acidic soddy-podzolic soils of the Kirov oblast, including with a lack of moisture: high yield, good technological properties of grain, and moderate resistance to helminthosporium leaf spots and root rot. [ABSTRACT FROM AUTHOR]

We present a strategy to discover recombinant monoclonal antibodies (mAbs) to specific cancers and demonstrate this approach using basal subtype breast cancers. A phage antibody library was depleted of antibodies to common cell surface molecules by incubation with luminal breast cancer cell lines, and then selected on a single basal-like breast cancer cell line (MDA-MB-231) for binding associated receptor-mediated endocytosis. Additional profiling against two luminal and four basal-like cell lines revealed 61 unique basal-specific mAbs from a pool of 1440 phage antibodies. The unique mAbs were further screened on nine basal and seven luminal cell lines to identify those with the greatest affinity, specificity, and internalizing capability for basal-like breast cancer cells. Among the internalizing basal-specific mAbs were those recognizing four transmembrane receptors (EphA2, CD44, CD73 and EGFR), identified by immunoprecipitation-mass spectrometry and yeast-displayed antigen screening. Basal-like breast cancer expression of these four receptors was confirmed using a bioinformatic approach, and expression microarray data on 683 intrinsically subtyped primary breast tumors. This overall approach, which sequentially employs phage display antibody library selection, antigen identification and bioinformatic confirmation of antigen expression by cancer subtypes, offers efficient production of high-affinity mAbs with diagnostic and therapeutic utility against specific cancer subtypes.

The successful development of mammalian cell culture for the production of therapeutic antibodies is a resource-intensive and multistage process which requires the selection of high performing and stable cell lines at different scale-up stages. Accordingly, science-based approaches exploiting biological information, such as metabolomics, can support and accelerate the selection of promising cell lines to progress. In fact, the integration of dynamic biological information with process data can provide valuable insights on the cell physiological changes as a consequence of the cultivation process. This work studies the industrial development of monoclonal antibodies at micro-bioreactor scale (Ambr®15) and aims at accelerating the selection of the better performing cell lines. To that end, we apply a machine learning approach to integrate time-varying process and biological information (i.e., metabolomics), explicitly exploiting their dynamics. Strikingly, cell line performance during the cultivation can be predicted from early process timepoints by exploiting the gradual temporal evolution of metabolic phenotypes. Furthermore, product titer is estimated with good accuracy at late process timepoints, providing insights into its relationship with underlying metabolic mechanisms and enabling the identification of biomarkers to be further investigated. The biological insights obtained through the proposed machine learning approach provide data-driven metabolic understanding allowing early identification of high performing cell lines. Additionally, this analysis offers the opportunity to identify key metabolites which could be used as biomarkers for industrially relevant phenotypes and onward fit into our commercial manufacturing platforms. • Machine learning to integrate process and biological dynamic information. • Accelerating the selection of better performing cell lines through data analytics. • Early prediction of cell lines performance from temporal evolution of metabolomics. [ABSTRACT FROM AUTHOR]

The fifth generation of wireless networks is expected to provide high capacity, low latency, high reliability, and massive connectivity services. Ultra-dense network (UDN) is a clear trend for enhancing capacity, coverage, and load balancing. In UDNs, the cell selection issue for moving vehicles should be addressed. In this paper, a cell selection strategy known as a dwell time estimation (DTE) scheme is proposed, which is based on Vehicle-to-Everything (V2X) communications. It selects small cells that have the longest dwell time in a vehicle’s direction to decrease handover (HO) rate. The proposed DTE scheme is evaluated using two datasets, which are 5G small cells and vehicles datasets, that were collected in the city of Los Angeles in California. The simulation result shows that our DTE algorithm outperforms other recent related schemes in terms of the mean number of HOs by up to 36.26% because it prolongs the dwell time of the vehicle within the serving small cell. Consequently, it reduces the average number of HO failures and unnecessary HOs. In addition, it gives improvements in terms of the average achievable downlink throughput and network energy efficiency. Furthermore, it has superiority over the other schemes in terms of the mean packet delay by up to 16.49%.

Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low- and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

Cell selection was used to obtain copper-resistant plants. Developed technologies for obtaining copper-resistant plants Agrostis stolonifera and Chrysanthemum carinatum can be applied to other plant species. We obtained copper-resistant plants Festuca rubra, Brachycome iberidifolia and Linum perenne. The concept of obtaining plants resistant to copper has been developed. This concept consists of two methods. The first method is applicable when calli is highly sensitive to copper. The second method is applicable when calli are moderately sensitive to copper. [ABSTRACT FROM AUTHOR]

Deicing reagents are priority soil pollutants in urban ecosystems. Sodium chloride is one of the priority deicing reagents. Sodium chloride is limiting the spread of lawn grass. We first showed the possibility of using environmental biotechnology in urban greening to obtain lawn grasses tolerant of sodium chloride. We have developed a cell selection technology to obtain salt-tolerant lawn grasses. A cell selection scheme with 1% sodium chloride was used. Most of the tested regenerants were more tolerant to NaCl than original plants. The descendants of the studied regenerants demonstrated the preservation of salt resistance. Most of the descendants of the regenerants Agrostis stolonifera retained high decorative qualities under salinity conditions. The tolerance remained in the next five generations. The descendants of the most salt-tolerant clones Agrostis stolonifera demonstrated resistance to 1% sodium chloride concentration in soil. These plants can serve as the basis for the creation of new salt-tolerant varieties. [ABSTRACT FROM AUTHOR]

Biosensors are powerful tools for improving microbial biosynthesis. For example, biosensors can be used to establish feedback control mechanisms to inhibit the growth of low performing cells in a microbial population. In this study, a previously developed biosensor-assisted cell selection system was further engineered to increase the cell selection pressure for improving the bioproduction of 4-hydroxybenzoate (4HB) in E. coli. First, additional sensor protein PobR binding sites were introduced to reduce the binding of PobR controlling the expression of the selection marker gene (antibiotic tetracycline resistance tetA), which stimulated the engineered cells to produce more 4HB to compensate for the tetA expression reduction due to the loss of the PobR binding. Second, the degradation of the PobR sensor protein was promoted using an E. coli ssrA-SspB system to press the cells to make more 4HB. Third, the dosage of antibiotic tetracycline controlling the selection strengths was dynamically increased during the cultivation to align the selection pressure elevation with the cell growth. Our findings show these strategies all effectively improved the 4HB biosynthesis in E. coli and thus demonstrate their potential of wider applications in microbial biosynthesis. • Three strategies were developed to increase the strength of a biosensor-based cell selection mechanism. • Biosynthesis of 4-hydroxybenzoate was used to test the effectiveness of the adopted strategies. • Extra sensor protein binding sites were added to impose higher cell selection pressure. • Accelerating the degradation of the sensor protein also stimulated the cells to produce more product. • The antibiotic dosage was dynamically increased to align the cell selection pressure elevation with the cell growth. [ABSTRACT FROM AUTHOR]

Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased. [ABSTRACT FROM AUTHOR]

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, the data provided by participants are not always trustworthy, and some malicious participants may intend to launch data positioning attacks, which raises a new challenge for cell selection. In this paper, to address these issues, we propose a trustworthy and cost-effective cell selection (TCECS) framework that takes cell heterogeneity and malicious participants into consideration simultaneously. To this end, we first offer to utilize an iterative statistical spatial interpolation technique to identify trustworthy participants with the help of a small portion of dedicated sensors. Furthermore, we employ the regularized mutual coherence (RMC) in compressive sensing (CS) theory to characterize the contribution to inference accuracy of measurements submitted by different trustworthy participants. Finally, the cell selection strategy, which consumes the least sensing cost while satisfying a given sensing quality, is determined via an RMC-constrained optimization problem. Extensive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adverse effects of malicious participants and outperforms the baselines with less sensing cost for the same required sensing quality. [ABSTRACT FROM AUTHOR]

Lawns and flower are major aesthetical and environmental elements of the urban ecosystem. However, harsh urban conditions such as pollution by heavy metals are unfavorable for plants. For example, copper is toxic for ornamental plants, limiting the spread of lawn grass and flowering plants. Therefore, here we hypothesized that plants could be adapted to urban conditions by breeding. We first showed the possibility of using environmental biotechnology in urban greening to obtain, tolerating copper flowering plants and lawn grasses. We tested the adaptation of creeping bentgrass (Agrostis stolonifera L.) and painted daisy (Chrysanthemum carinatum Schousb.) to сopper. We measured Cu resistance in the next generations of those plant species. Results show that some next generations of plant regenerants have increased resistance up to 100 mg/kg Cu for Agrostis stolonifera, and up to 30 mg/kg for Chrysanthemum carinatum. Our findings thus imply that city plants may be adapted and improved by сell selection. Our approach thus represents a novel biotechnology consisting of adapting plants to pollution by сell selection. [ABSTRACT FROM AUTHOR]

Most biopharmaceutical proteins are produced in mammalian cells because they have the advantageous capacity for protein folding, assembly, and posttranslational modifications. To satisfy the increasing demand for these proteins for clinical purposes and studies, traditional methods to improve protein productivity have included gene amplification, host cell engineering, medium optimization, and screening methods. However, screening and selection of high-producing cell lines remain complex and time consuming. In this study, we established a glycosylphosphatidylinositol (GPI)-anchored protein with a selenocysteine (GPS) system to select cells producing high levels of target secretory proteins. Recombinant lysosomal acid lipase (LIPA) and α-galactosidase A (GALA) were fused with a GPI attachment signal sequence and a selenocysteine insertion sequence after an in-frame UGA codon. Under these conditions, most of the recombinant proteins were secreted into the culture medium, but some were found to be GPI-anchored proteins on the cell surface. When sodium selenite was supplied into the culture medium, the amount of GPI-anchored LIPA and GALA was increased. High-expressing cells were selected by detecting surface GPI-anchored LIPA. The GPI-anchored protein was then eliminated by knocking out the GPI biosynthesis gene PIGK , in these cells, all LIPA was in secreted form. Our system provides a promising method of isolating cells that highly express recombinant proteins from large cell populations. [ABSTRACT FROM AUTHOR]

The growth of the information, communication and telecommunication (ICT) industry is on the ascendancy. The proliferation of mobile devices and other web-based applications has compelled ICT industries to deploy more resource towards capacity expansion and improved quality of service. This singular act has triggered an upsurge in both energy consumption and carbon emission within the mobile cellular industry. As a result, current research efforts within the mobile wireless network are tailored towards improving the energy efficiency of network components and systems. One promising solution comes through the deployment of a heterogeneous network. In this work, the impact of cell selection techniques on the overall network performance of a heterogeneous network is investigated. The overall simulation results reveal that efficient cell selection influences a better performance in energy efficiency and area energy efficiency of such a network. [ABSTRACT FROM AUTHOR]

Many therapeutic targets are cell surface receptors, which can be challenging antigens for antibody generation. For many therapeutic applications, one needs antibodies that not only bind the cell surface receptor but also are internalized into the cell. This allows use of the antibody to deliver various payloads into the cell to achieve a therapeutic effect. Phage antibody technology has proven a powerful tool for the generation and optimization of human antibodies to any antigen. While applied to the generation of antibodies to purified proteins, it is possible to directly select cell binding and internalizing antibodies on cells. Potential advantages of this approach include: cell surface receptors are in native conformation on intact cells while this might not be so for recombinant proteins; antibodies can be selected for both cell binding and internalization properties; the antibodies can be used to identify their tumor associated antigens; and such antibodies can be used for human treatment directly since they are human in sequence. This review will discuss the factors that impact the successful selection of cell binding and internalizing antibodies. These factors include the cell types used for selection, the impact of different phage antibody library formats, and the specific selection protocols used.

To satisfy the high data demands in future cellular networks, an ultra-densification approach is introduced to shrink the coverage of base station (BS) and improve the frequency reuse. The gain in capacity is expected but at the expense of increased interference, frequent handovers (HOs), increased HO failure (HOF) rates, increased HO delays, increase in ping pong rate, high energy consumption, increased overheads due to frequent HO, high packet losses and bad user experience mostly in high-speed user equipment (UE) scenarios. This paper presents the general concepts of radio access mobility in cellular networks with possible challenges and current research focus. In this article, we provide an overview of HO management in long-term evolution (LTE) and 5G new radio (NR) to highlight the main differences in basic HO scenarios. A detailed literature survey on radio access mobility in LTE, heterogeneous networks (HetNets) and NR is provided. In addition, this paper suggests HO management challenges and enhancing techniques with a discussion on the key points that need to be considered in formulating an efficient HO scheme.

As the number of users in cellular mobile networks increases, cellular network changed from the previous single architecture to heterogeneous architecture, base station energy consumption also showed an exponential growth. Energy harvesting is an promising technology to save on-grid power and reduce carbon emission in wireless communications. The cell selection in the HetNets is the critical factor of affecting network performance. In this paper, from the energy harvesting, energy cooperation, heterogeneous networks and other aspects, a cell selection algorithm is proposed, which can compromise energy harvesting, energy consumption, load balancing and other criteria. The simulation results show that this algorithm can reduce the base station′s demand for grid energy, increase the possibility of user selection, and satisfy the fairness and validity. It′s applicable to 5G green communications.

Primary B- and T-lymphocytes and neoplastic cells of B- and T-cell origin are particularly hard to transfect with plasmids. Thus, functional and molecular studies in immunology and oncohematology require selection of cells after plasmid transduction, assuring obtaining homogeneous cell populations expressing plasmid-delivered molecules. The selection is usually achieved by incorporation of an antibiotic resistance gene or fluorescent protein into a plasmid and subsequent antibiotic selection or fluorescence-activated cell sorting (FACS). However, these cell sorting methods have significant drawbacks that limit their utilization. An ideal system should be fast, cheap and ensure efficient selection of transfected cells. In an attempt to deliver a sorting system meeting these requirements, we have generated a plasmid for retroviral gene delivery and expression, containing a recombinant human-murine CD4 gene, with a truncated intracellular domain to prevent undesired signaling events. After retroviral infection, cells expressing hmCD4 are selected by immunomagnetic anti-mCD4 beads. The system offers a very robust, efficient, fast and cost-saving cell separation solution. The vector is available for the academic community upon request.

Accumulation of cadmium (Cd) in barley grain (Hordeum vulgare L.) and its subsequent transmission through food chains to humans and animals is a serious environmental problem. The creation of stress-tolerant varieties to reduce crop losses and bioaccumulation of toxic ions can contribute to the preservation of grain quality and increase the productivity of barley on cadmium-contaminated soils. In the process of cell selection in 2017 from the original genotype of barley 8730 x Omsk in callus cultures somaclonal variants (RA) and conducted among them the selection of toxicity-resistant 15 mg/l cadmium ion lines (RACd) were obtained. Plants regenerated from callus were compared with the original genotype by physiological, biochemical and productive features, cadmium bioaccumulation in the conditions of vegetation experience. Plants were grown in normal conditions and against a provocative background of cadmium in 2017-2018. Symptoms of oxidative stress in plants were evaluated by the content of Malon dialdehyde in the leaves, the activity of superoxide dismutase, the accumulation of anthocyanins. Under stress, barley lines selected as a result of cell selection (in the medium with 15 mg/l Cd2+), differed from regenerants obtained under normal conditions( without Cd2+ ), and the initial genotype with higher antioxidant activity, chlorophyll (64 and 76%) and carotenoid (60 and 64%, respectively) content in leaves, seed productivity (1.3 and 4.5 times correspondently) and cadmium bioaccumulation in roots (5 and 100%, respectively). The obtained results indicate the feasibility of using cell engineering in breeding programs to improve the resistance of barley to cadmium toxicity.

Wireless communication between public safety officers is very important to transmit voice or data during emergency crises. When the public communication networks cannot provide services during crises, disasters, and high traffic cases, Professional or private mobile radio (PMR) such as Association of Public Safety Communications Officials (APCO25) conventional systems are needed to improve the service quality and to provide uninterrupted service to the users. In this paper, we propose traffic-based cell selection algorithms for the APCO25 conventional systems to attach users to base stations in a balanced manner to reduce waiting time while establishing a connection. The simulation results of the proposed traffic load–based cell selection algorithms are illustrated in terms of the RSSI measurements counter, the number of connection requests, the average waiting time, and the number of re-selections for the APCO25 conventional systems. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cells (MSC) make up less than 1% of the bone marrow (BM). Several methods are used for their isolation such as gradient separation or centrifugation, but these methodologies are not direct and, thus, plastic adherence outgrowth or magnetic/fluorescent-activated sorting is required. To overcome this limitation, we investigated the use of a new separative technology to isolate MSCs from BM; it label-free separates cells based solely on their physical characteristics, preserving their native physical properties, and allows real-time visualization of cells. BM obtained from patients operated for osteochondral defects was directly concentrated in the operatory room and then analyzed using the new technology. Based on cell live-imaging and the sample profile, it was possible to highlight three fractions (F1, F2, F3), and the collected cells were evaluated in terms of their morphology, phenotype, CFU-F, and differentiation potential. Multipotent MSCs were found in F1: higher CFU-F activity and differentiation potential towards mesenchymal lineages compared to the other fractions. In addition, the technology depletes dead cells, removing unwanted red blood cells and non-progenitor stromal cells from the biological sample. This new technology provides an effective method to separate MSCs from fresh BM, maintaining their native characteristics and avoiding cell manipulation. This allows selective cell identification with a potential impact on regenerative medicine approaches in the orthopedic field and clinical applications.

A LTE-advanced heterogeneous network consists of many macrocells, and within each macrocell, there can have many picocells and user equipments (UEs). A UE within a macrocell can request resource blocks (RBs) from either the macrocell or a picocell within the macrocell. In this paper, two optimal problems are defined for the association of UEs, RBs, and heterogeneous cells. We show that both problems are equivalent to a 0/1 knapsack problem and, therefore, can be solved in pseudo-polynomial time by dynamic programming. Based on above two optimal problems, we, then, propose a cell selection and resource allocation (CSRA) scheme to solve the problem of allocating a UE to its macrocell or to a picocell within the macrocell. The design goal of picocells is to unload the traffic for the macrocell. When there are available RBs and UEs within the picocell, the CSRA scheme offloads UEs to the picocell to release the most number of RBs for the macrocell. Only when there are not enough RBs within a picocell and there are available RBs within the macrocell, for load balancing, the CSRA scheme moves some UEs to the macrocell. The CSRA scheme stops whenever the picocell is not overloaded or the macrocell does not have available RBs. Simulation results show that our proposed scheme performs better than the cell range expansion scheme in terms of goodput, loss rate, and delay.

The densification of small cells in heterogeneous networks (HetNets) causes huge energy consumption and severe network interference. To fully exploit the potential of new network architecture, the cell selection (CS) in such HetNets should couple with reducing power consumption and network interference. To this end, we jointly perform cell activation and selection (CAS) to maximize the network energy efficiency (EE) under users' long-term rate constraints. The formulated problem is in a mixed-integer fractional form and hard to tackle. We need to transform it into a parametric subtractive form, by which we reach its solution through a three-layer iterative algorithm. The first layer searches an EE parameter using a bisection method; the second layer alternately optimizes CAS indices; the third layer solves CS and cell activation (CA) problems using dual decomposition and fixed point iteration, respectively. At last, we give some complexity and convergence analyses for the designed algorithm, and investigate the impacts of different network parameters on system performance. The simulation results show that the CA introduced in CS is a good option to reduce energy consumption and network interference.

In this paper, we present a price-based approach for offloading macro users to small-cells in a two-tier heterogeneous network (HetNet). We have constructively exploited the small-cell density in a HetNet to harness offloading opportunities for macro users (MUs), the goal of which is to solve two problems simultaneously. It aims to shun the MUs, who have a lower received data rate due to the interference perceived from the small-cell tier. The scheme also fends off congestion in a macrocell, by offloading macro users to the small-cell tier. We have proposed a novel threshold pricing scheme, which a macrocell adopts, with a view to influencing low data rate MUs to join a small-cell network. Small-cell networks also charge a price, which includes an access price and an interference compensation price, proportional to the number of MUs who choose the small-cells instead of the macrocellular network. We assume that the small-cells adopt existing cell range expansion techniques to accommodate MUs. We formulate an evolutionary game to model and analyze the behavioral dynamics of the large number of MUs under the proposed pricing strategies of both networks. Replicator dynamics is used to find the evolutionary equilibrium of the evolutionary game. Sequentially, we provide the proof of the existence, uniqueness and stability of the evolutionary equilibrium through extensive analysis. Numerical results are provided to demonstrate that the proposed pricing strategies are able to shape the network dynamics by fine-tuning the rate-threshold and price. The ability to control the macrocell population share by itself with an application of the proposed pricing scheme remains the prime contribution of this paper.