Interaction of neuronal nitric oxide synthase with alpha(1)-adrenergic receptor subtypes in transfected HEK-293 cells

BACKGROUND: The C-terminal four amino acids (GEEV) of human α(1A)-adrenergic receptors (ARs) have been reported to interact with the PDZ domain of neuronal nitric oxide synthase (nNOS) in a yeast two-hybrid system. The other two α(1)-AR subtypes have no sequence homology in this region, raising the possibility of subtype-specific protein-protein interactions. RESULTS: We used co-immunoprecipitation and functional approaches with epitope-tagged α(1)-ARs to examine this interaction and the importance of the C-terminal tail. Following co-transfection of HEK-293 cells with hexahistidine/Flag (HF)-tagged α(1A)-ARs and nNOS, membranes were solubilized and immunoprecipitated with anti-FLAG affinity resin or anti-nNOS antibodies. Immunoprecipitation of HFα(1A)-ARs resulted in co-immunoprecipitation of nNOS and vice versa, confirming that these proteins interact. However, nNOS also co-immunoprecipitated with HFα(1B)- and HFα(1D)-ARs, suggesting that the interaction is not specific to the α(1A) subtype. In addition, nNOS co-immunoprecipitated with each of the three HFα(1)-AR subtypes which had been C-terminally truncated, suggesting that this interaction does not require the C-tails; and with Flag-tagged β(1)- and β(2)-ARs. Treatment of PC12 cells expressing HFα(1A)-ARs with an inhibitor of nitric oxide formation did not alter norepinephrine-mediated activation of mitogen activated protein kinases, suggesting nNOS is not involved in this response. CONCLUSIONS: These results show that nNOS does interact with full-length α(1A)-ARs, but that this interaction is not subtype-specific and does not require the C-terminal tail, raising questions about its functional significance

Obtaining and Structural Characterization of M-type Hexaferrites Doped with Two Cations in the Fe3+ Sites

A study of the microstructural and structural properties of M-type barium hexaferrites (BaM) samples doped with two dopants in the Fe3+ sites: (Co3+, Al3+), (Co2+, Ti4+) and (Co2+, Sn4+) is reported. The samples were obtained using the conventional ceramic method. The structure was investigated by using of X-ray diffraction (XRD) to determine the dopant distribution in the Fe3+ sites

From MFN to SFN: Performance Prediction Through Machine Learning

In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97

Artificial Intelligence Aided Low Complexity RRM Algorithms for 5G-MBS

For the upcoming 5G-Advanced, the multicast/broadcast services (5G-MBS) capability is one of the most appealing use cases. The effective integration of point-to-multipoint communication will address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper proposes novel approaches for the dynamic access technique selection and resource allocation for multicast groups (MGs) subject to the 5G-MBS paradigm. Our proposal is oriented to address and contextualize the complexity associated with multicast radio resource management (RRM) and the implications of fast variations in the reception conditions of the MG members. We propose a solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-means clustering for group-oriented detection and splitting, a classifier for selecting the most suitable multicast access technique, and a final resource allocation algorithm. To choose the multicast access technique that better fits the specific reception conditions of the users, we evaluate heuristic strategies and machine learning (ML) multiclass classification solutions. We consider the conventional multicast scheme (MCS) and subgrouping based on orthogonal/non-orthogonal multiplex access (OMA/NOMA) as access techniques. We assess the effectiveness of our solution in terms of the quality of service (QoS) parameters and complexity. The proposed technical solution is validated through extensive simulation for a single-cell 5G-MBS use case in the microwave (\mu Wave) and millimeter wave (mmWave) band with different mobility behaviors

Network Selection Over 5G-Advanced Heterogeneous Networks Based on Federated Learning and Cooperative Game Theory

5G-Advanced and Beyond claims a 3D ecosystem with cooperation between terrestrial and non-terrestrial networks to achieve seamless coverage, improve capacity, and enable advanced applications with strict quality of service (QoS) requirements. This complex environment requires a disaggregated Radio Access Network (RAN) deployment with open interfaces, such as the architecture promoted by the O-RAN Alliance. This architecture, supporting the slicing paradigm, is a prominent solution to guarantee dynamism and differentiated traffic management. Furthermore, intelligence is critical for future wireless networks to enable Machine Learning (ML)-based optimization for autonomous RANs, handling ultra-dense heterogeneous environments, and adapting to numerous scenarios. This paper presents an enhanced Dynamic Radio Access Network Selection (eDRANS) algorithm based on Federated Double Deep Q-Network (F-DDQN) and inserted in the novel O-RAN architecture. The proposal selects the most suitable base station (BS) to satisfy multiple service requests, optimizing QoS and slicing resource utilization. Moreover, the solution employs a Cooperative Game Theory (CGT) approach to manage resources in overload situations. This load-balancing process enables the acceptance of new clients without abruptly degrading the active users' perception. eDRANS is adapted to diverse network conditions, multiple service constraints, and several user types with different priorities and mobility behaviors. The proposal is validated through network-level simulations, recreating a heterogeneous environment composed of terrestrial-airborne nodes and using the Max-SINR criterion, a heuristic algorithm, and centralized and distributed ML solutions as benchmarks. Results show that eDRANS correctly learns during multiple trial-and-error interactions with the environment, fulfilling the Service Level Agreement (SLA) and maximizing user satisfaction

Biotransformation of a tetrahydrofuran lignan by the endophytic fungus Phomopsis Sp.

The biotrasformation of the tetrahydrofuran lignan, (-)-grandisin, by the endophitic fungus Phomopsis sp, obtained from Viguiera arenaria, led to the formation of a new compound determined as 3,4-dimethyl-2-(4'-hydroxy-3',5'-dimethoxyphenyl)-5-methoxy-tetrahydrofuran. The metabolite was evaluated against the parasite Trypanosoma cruzi, the causative agent of Chagas's disease, and showed a trypanocidal activity (IC50 9.8 μmol L-1) similar to the natural precursor (IC50 3.7 μmol L-1).A biotransformação da lignana tetraidrofurânica, (-)-grandisina, pelo fungo endofítico Phomopsis sp, obtido de Viguiera arenaria, conduziu à formação de um novo metabólito caracterizado como 3,4-dimetil-2-(4'-hidróxi-3',5'-dimetóxifenil)-5-metóxi-tetraidrofurano. O metabólito foi analisado contra o parasita Trypanosoma cruzi, o agente causador da doença de Chagas, e mostrou uma atividade tripanocida (IC50 9,8 μmol L-1) similar ao precursor natural (IC50 3,7 μmol L-1).Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Shortening the lipid A acyl chains of Bordetella pertussis enables depletion of lipopolysaccharide endotoxic activity

Whooping cough, or pertussis, is an acute respiratory infectious disease caused by the Gram-negative bacterium Bordetella pertussis. Whole-cell vaccines, which were introduced in the fifties of the previous century and proved to be effective, showed considerable reactogenicity and were replaced by subunit vaccines around the turn of the century. However, there is a considerable increase in the number of cases in industrialized countries. A possible strategy to improve vaccine-induced protection is the development of new, non-toxic, whole-cell pertussis vaccines. The reactogenicity of whole-cell pertussis vaccines is, to a large extent, derived from the lipid A moiety of the lipopolysaccharides (LPS) of the bacteria. Here, we engineered B. pertussis strains with altered lipid A structures by expressing genes for the acyltransferases LpxA, LpxD, and LpxL from other bacteria resulting in altered acyl-chain length at various positions. Whole cells and extracted LPS from the strains with shorter acyl chains showed reduced or no activation of the human Toll-like receptor 4 in HEK-Blue reporter cells, whilst a longer acyl chain increased activation. Pyrogenicity studies in rabbits confirmed the in vitro assays. These findings pave the way for the development of a new generation of whole-cell pertussis vaccines with acceptable side effects

Dynamic Scheduling and Optimal Reconfiguration of UPF Placement in 5G Networks

Multi-access Edge Computing (MEC) is a key technology in the road to 5G and beyond networks. Significant reductions in both latency and backhaul traffic can be achieved by placing server applications, and network functions at the network edge. However, this implies new challenges for their dynamic placement and management. In this paper, we tackle the problem of dynamic placement reconfiguration of 5G User Plane Functions (UPFs) in a MEC ecosystem to adapt to changes in user locations while ensuring QoS and network operator expenditures reduction. In this vein, an Integer Linear Programming (ILP) solution is proposed to determine the optimal UPF placement configuration (e.g., number of UPFs and user-UPF mapping) by considering several cost components along with service requirements. Moreover, a scheduling technique based on Optimal Stopping Theory (OST) is presented to decide the optimal reconfiguration time according to instantaneous values of latency violations and established QoS thresholds. Extensive simulation results demonstrate their effectiveness, achieving significant improvements in metrics such as number of re-computation events, reconfiguration costs, and number of latency violations over time

Symbiotic skin bacteria as a source for sex-specific scents in frogs

Amphibians are known to possess a wide variety of compounds stored in their skin glands. While significant progress has been made in understanding the chemical diversity and biological relevance of alkaloids, amines, steroids, and peptides, most aspects of the odorous secretions are completely unknown. In this study, we examined sexual variations in the volatile profile from the skin of the tree frog Boana prasina and combined culture and culture-independent methods to investigate if microorganisms might be a source of these compounds. We found that sesquiterpenes, thioethers, and methoxypyrazines are major contributors to the observed sex differences. We also observed that each sex has a distinct profile of methoxypyrazines, and that the chemical origin of these compounds can be traced to a Pseudomonas sp. strain isolated from the frog´s skin. This symbiotic bacterium was present in almost all individuals examined from different sites and was maintained in captive conditions, supporting its significance as the source of methoxypyrazines in these frogs. Our results highlight the potential relevance of bacteria as a source of chemical signals in amphibians and contribute to increasing our understanding of the role that symbiotic associations have in animals.Fil: Brunetti, Andrés Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; Argentina. Universidade de Sao Paulo; BrasilFil: Lucio Lyra, Mariana. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Melo, Weilan G. P.. Universidade de Sao Paulo; BrasilFil: Andrade, Laura Elena. Universidade de Sao Paulo; BrasilFil: Palacios Rodríguez, Pablo. Universidad de los Andes; ColombiaFil: Prado, Bárbara M.. Universidade de Sao Paulo; BrasilFil: Baptista Haddad, Célio Fernando. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Tallarico Pupo, Monica. Universidade de Sao Paulo; BrasilFil: Peporine Lopes, Norberto. Universidade de Sao Paulo; Brasi