Processing Capacity Research Articles

Numerical Simulations and Bifurcation of Ca2+ Oscillatory Behaviour in the Connection of Neurons and Astrocytes.

Extensive research has demonstrated that astrocytes actively participate in the regulation of synaptic communication. To examine the dynamic behavior of the model, a neuron-astrocyte model has been solved, and a bifurcation analysis has been performed. This paper uses the equilibrium point, stability theory, and the center manifold theorem to theoretically investigate the dynamical analysis of Ca2+ oscillations in the cytosol. The connections at tripartite synapses between the cells have been modeled using IP3 and 2-AG. A mathematical model is used to depict the overall framework of bifurcation and induced Ca2+ dynamics. The differences in the presence and disappearance of Ca2+ oscillations are partially explained by two subcritical Hopf bifurcation points, according to the results. Communication between the cells occurs through the oscillations of Ca2+ concentration. Furthermore, numerical simulations are conducted to confirm the efficacy of the suggested approach. Thus, our findings imply that neuron-astrocyte crosstalk plays a fundamental role in generating a variety of neuronal activities, thereby improving the brain's capacity for information processing.

A Markov regime-switching event response model: beef price spread response to processing capacity shocks

AbstractThis research introduces a new method for event studies in time-series analysis named the Markov regime-switching event response model (MS-ERM). The MS-ERM is a comprehensive approach that integrates two different event study approaches: 1) measuring the impact of an event through structural shift and 2) measuring the impact via additional distributional components. As an empirical application, the study measures the impact of beef-packing plant closures on the weekly live-to-cutout beef price spread. The results indicate that the MS-ERM is a promising tool for event studies, particularly when an empirical dataset has both groups of events that cause and do not cause structural changes.

Trend and Instability Analysis of Milk Prices in Major Markets of India

The growth rate of production and per capita availability of milk and growth rate, variability and convergence/divergence of retail prices of milk in the major consuming markets of India has been examined in this article using annual production and per capita availability of milk data for the period from 1980-81 to 2022-23 and deseasonalised monthly milk retail prices from 1975-76 to 2022-23 was utilized to study the growth rate, variability and convergence/divergence of retail prices of milk. The entire period was further divided into two sub-periods and Compound Annual Growth Rate (CAGR), Coefficient of Variation around the trend and Bandyopadhyay (1989) methodology were applied to examine the growth rate, variability, instability and convergence/divergence of milk retail prices. The study concluded that the production and per capita availability of milk was increasing continuously. The highest average milk retail price was recorded in Delhi market during TE2022. The growth rate of milk retail prices were increased in Delhi and Kolkata markets while in Mumbai and Chennai markets, it was reduced from sub-period I to sub-period II. The variability of milk retail prices were reduced from sub-period I to sub-period II in all the selected markets. The Mumbai market exhibited highest reduction in variability in milk retail prices while the Chennai market showed lowest reduction in retail price of milk from sub-period I to sub-period II. The positive and negative deviation trend lines were diverging during sub-period I while they were converging during sub-period II. This indicated that instability of milk retail price was reducing during sub-period II. The reduction of variability and instability in milk retail prices was caused by the increased production and per capita availability of milk, increased milk processing capacity and improvement in marketing infrastructure in the recent time.

Reducing Makespan and Enhancing Resource Usage in Cloud Computing With ESJFP Method: A New Dynamic Approach

ABSTRACTA well‐designed web‐based tool or application allows consumers to access on‐demand services on a pay‐per‐use basis via the internet in the cloud computing service paradigm. Cloud computing continues to be a leading technology trend, with a primary focus on optimizing and enhancing end‐user applications. The increasing challenge of meeting the diverse needs of clints for service providers is propelling the development of load scheduling algorithms. Some of the current scheduling algorithms used in cloud computing include First Come First Serve (FCFS), Shortest Job First (SJF) and Round Robin (RR). The response time and makespan—the interval between the start and end times of consecutive tasks on the same machine—are the two most crucial load balancing elements. Response time refers to the duration a server takes to respond to a client's request. Measured in milliseconds, this timer starts when a client sends a request and stops when the server sends its initial response. This study examines many load balancing methods and suggests improvements for cloud computing's Shortest Job First (SJF) methodology. To minimize makespan and maximize resource usage, we provide a solution that is Enhanced Shortest Job First with Priority (ESJFP) load scheduling algorithms. Under the ESJFP method, computers with more processing power are assigned the longest tasks with higher MIPS (million instructions per second) needs, while machines with lesser processing capacity are assigned the shortest jobs with lower MIPS requirements. By giving equal priority to all activities, this technique makes sure that neither high‐MIPS nor low‐MIPS jobs have to wait an extended period of time for resource allocation.

The Gasification and Pyrolysis of Biomass Using a Plasma System

This research paper analyzes the use of plasma technology to process biomass in the form of dried, mixed animal manure (dung containing 30% moisture). The irrational use of manure as well as huge quantities of it can negatively impact the environment. In comparison to biomass fermentation, the plasma processing of manure can greatly enhance the production of fuel gas, primarily synthesis gas (CO + H2). The organic part of dung, including the moisture, is represented by carbon, hydrogen, and oxygen with a total concentration of 95.21%, while the mineral part is only 4.79%. A numerical analysis of dung plasma gasification and pyrolysis was conducted using the thermodynamic code TERRA. For 300–3000 K and 0.1 MPa pressure, the dung gasification and pyrolysis were calculated with 100% dung + 25% air and 100% dung + 25% nitrogen, respectively. Calculations were performed to determine the specific energy consumption of the process, the composition of the products of gasification, and the extent of the carbon gasification. At 1500 K, the dung gasification and pyrolysis consumed 1.28 and 1.33 kWh/kg of specific energy, respectively. A direct-current plasma torch with a power rating of 70 kW and a plasma reactor with a dung processing capacity of 50 kg/h were used for the dung processing experiments. The plasma reactor consumed 1.5 and 1.4 kWh/kg when pyrolyzing and gasifying the dung. A maximum temperature of 1887 K was reached in the reactor. The plasma pyrolysis of dung and the plasma–air gasification of dung produced gases with specific heats of combustion of 10,500 and 10,340 kJ/kg, respectively. Calculations and experiments on dung plasma processing showed satisfactory agreement. In this research, exergy analysis was used to quantify the efficiency of the plasma gasification of biomass. One of the research tasks was to develop a methodology and establish standards for the further standardization of monitoring the toxic emissions of dioxins, furans, and Benzo[a]pyrene.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

Air pollution under formal institutions: The role of distrust environment

Formal trust is an important formal institution that may significantly impact the environment. This study uses regional distrust environment as a reverse proxy variable for formal trust and studies the impact of formal trust on corporate sulfur dioxide emissions. This study finds that the environment of distrust significantly increases the sulfur dioxide emission levels of enterprises, which means that formal trust affects the environmental management strategies of enterprises. This study also finds that some other formal institutional factors, which include marketization, the development of intermediate organizations, the legal system environment, and GDP levels, have moderating effects on the impact of distrust environment on corporate sulfur dioxide emissions. In addition, climatic conditions including temperature, humidity, and precedence, as well as the location of the enterprise, have certain moderation effects. Mechanism analysis indicates that distrust environment affects corporate sulfur dioxide emissions through the increase in coal sulfur content in enterprise production, the decrease in exhaust gas processing capacity, the reduction in financing capacity, and the decline in social and environmental responsibilities. Finally, this study finds through further analysis that the local government appears to have noticed this negative impact, and the regions with a distrust environment tend to increase their environmental regulation intensity.

Visual Processing by Hierarchical and Dynamic Multiplexing.

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

Inertial and Deterministic Lateral Displacement Integrated Microfluidic Chips for Epithelial-Mesenchymal Transition Analysis.

With the aim of efficiently sorting rare circulating tumor cells (CTCs) from blood and minimizing damage to CTCs during isolation, we constructed an inertia-assisted single-cell focusing generator (I-SCF) and a water droplet deterministic lateral displacement cell sorting (D-DLD) microfluidic system (IDIC) based on different sizes, the device is initially sorted by a continuous fluid swing and Dean flow-assisted helical micromixers, then flows through a droplet shaped DLD region, enabling single-cell focused sequencing and precise separation, improving cell separation efficiency (>95%) and purity, while ensuring a high single cells survival rate of more than 98.6%. Subsequently, breast cancer cell lines were run through our chip, and then the downstream epithelial-mesenchymal transition (EMT) process induced by TGF-β was detected, and the levels of three proteins, EpCAM, PD-L1, and N-cadherin, were analyzed to establish the relationship between PD-L1 and the EMT process. Compared with other analytical techniques such as the filtration method, the enrichment method and immunoaffinity capture methods, this method not only ensures the separation efficiency and purity, but also ensures the cell activity, and avoids missing the different results caused by the heterogeneity of CTCs due to the isolation of high purity (84.01%). The device has a high throughput processing capacity (5 mL of diluted whole blood/∼2.8 h). By using the chip, we can more easily and conveniently predict tumor stage and carry out cancer prevention and treatment in advance, and it is expected to be further developed into a clinical liquid biopsy technology in the future.

Impact of sample processing delays on plasma markers of inflammation, chemotaxis, cell death, and blood coagulation.

Biosampling studies in critically ill patients traditionally involve bedside collection of samples followed by local processing (ie. centrifugation, aliquotting, and freezing) and storage. However, community hospitals, which care for the majority of Canadian patients, often lack the infrastructure for local processing and storage of specimens. A potential solution is a "simplified" biosampling protocol whereby blood samples are collected at the bedside and then shipped to a central site for processing and storage. One potential limitation of this approach is that delayed processing may alter sample characteristics. To determine whether delays in blood sample processing affect the stability of cytokines (IL-6, TNF, IL-10, IFN-γ), chemokines (IL-8, IP-10, MCP-1, MCP-4, MIP-1α, MIP-1β), cell-free DNA (cfDNA) (released by dying cells), and blood clotting potential in human blood samples. Venous blood was collected into EDTA and citrate sample tubes and stored at room temperature (RT) or 4°C for progressive intervals up to 72 hours, prior to processing. Plasma cytokines and chemokines were quantified using single or multiplex immunoassays. cfDNA was measured using Picogreen DNA Quantification. Blood clotting potential was measured using a thrombin generation assay. Blood samples were collected from 9 intensive care unit (ICU) patients and 7 healthy volunteers. Admission diagnoses for the ICU patients included sepsis, trauma, ruptured abdominal aortic aneurysm, intracranial hemorrhage, gastrointestinal bleed, and hyperkalemia. After pre-processing delays of up to 72 hours at RT or 4°C, no significant changes were observed in plasma cytokines, chemokines, cfDNA, or thrombin formation. Delayed sample processing for up to 72 hours at either RT or 4°C did not significantly affect cytokines, chemokines, cfDNA, or blood clotting potential in plasma samples from healthy volunteers and ICU patients. A "simplified" biosampling protocol is a feasible solution for conducting biosampling research at hospitals without local processing capacity.

Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption. With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs. These advantages have resulted, at the moment, in more than 900 AI-based devices being approved, today, by regulatory entities, for clinical use, with similar to improved performance and efficiency compared to traditional technologies. However, issues such as model generalization, bias, transparency, interpretability, accountability, and data privacy remain significant barriers for broad adoption of these technologies. AI shows great promise in enhancing CVD care through more accurate and efficient approaches. Yet, widespread adoption is hindered by unresolved concerns of interested stakeholders. Addressing these challenges is crucial for fully integrating AI into clinical practice and shaping the future of CVD prevention, diagnosis and treatment.

Biogenesis and Regulation of the Freeze–Thaw Responsive microRNA Fingerprint in Hepatic Tissue of Rana sylvatica

Background: Freeze-tolerant animals undergo significant physiological and biochemical changes to overcome challenges associated with prolonged whole-body freezing. In wood frog Rana sylvatica (now Lithobates sylvaticus), up to 65% of total body water freezes in extracellular ice masses and, during this state of suspended animation, it is completely immobile and displays no detectable brain, heart, or respirometry activity. To survive such extensive freezing, frogs integrate various regulatory mechanisms to ensure quick and smooth transitions into or out of this hypometabolic state. One such rapid and reversible regulatory molecule capable of coordinating many aspects of biological functions is microRNA. Herein, we present a large-scale analysis of the biogenesis and regulation of microRNAs in wood frog liver over the course of a freeze–thaw cycle (control, 24 h frozen, and 8 h thawed). Methods/Results: Immunoblotting of key microRNA biogenesis factors showed an upregulation and enhancement of microRNA processing capacity during freezing and thawing. This was followed with RT-qPCR analysis of 109 microRNA species, of which 20 were significantly differentially expressed during freezing and thawing, with the majority being upregulated. Downstream bioinformatics analysis of miRNA/mRNA targeting coupled with in silico protein–protein interactions and functional clustering of biological processes suggested that these microRNAs were suppressing pro-growth functions, including DNA replication, mRNA processing and splicing, protein translation and turnover, and carbohydrate metabolism. Conclusions: Our findings suggest that this enhanced miRNA maturation capacity might be one key factor in the vital hepatic miRNA-mediated suppression of energy-expensive processes needed for long-term survival in a frozen state.

Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC

This study aims to redefine the capabilities of the service supply chain in the Iranian oil and gas industry, where the concept of service chains has remained underdeveloped due to a traditional focus on conventional service chains. It seeks to compare the capabilities of the oil and gas service supply chain to those in other sectors and explore whether applied research is necessary to improve service chain performance. The study was conducted in three stages, involving ten petroleum and natural gas experts. The first stage focused on identifying key capabilities, yielding seven significant capabilities and 28 critical sub-capabilities. In the second stage, the SWARA fuzzy hybrid approach was employed to weigh and prioritize these capabilities, while the fuzzy MABAC methodology was used for strategic decision-making. Finally, the model’s sensitivity was assessed using fuzzy methods to validate the findings. The results highlight the highest priority capability for service providers: the ability to utilize information for updating information processing capacity in making decisions within the service supply chain. The selected location, G1, emerged as a key area of focus. This approach presents a novel method for optimizing service supply chain locations within the oil and gas sector. This paper introduces a unique approach to selecting optimal service supply chain locations in the oil and gas industry, addressing critical gaps in previous research. Employing advanced fuzzy hybrid methodologies provides valuable insights into improving service chain capabilities, offering a competitive advantage for companies operating in this sector.

Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.

Fuzzy flexible job shop dynamic scheduling considering machine remaining processing capacity

Dynamic fuzzy flexible job shop scheduling (DFFJSP) is significant in the medical equipment industry. Generally the dynamic flexible job shop scheduling problem considers a single dynamic factor or ignores resources such as machine itself also plays a huge role in dealing dynamic issues. Thus fuzzy processing time and machine breakdowns are considered in this paper. In order to copy with two problems mentioned above, the model of DFFJSP containing two objectives of makespan and energy consumption is established. To solve this problem, the remaining processing capacity of broken machine is applied to weaken the impacts of machine breakdowns and a multi-objective evolutionary algorithm (NG-WOA) is proposed, which integrating NSGA-II and whale optimal algorithm (WOA). Furthermore, the joining of variable neighborhood search makes NG-WOA skip the local optimum solution in time. Through experiments, the validity of remaining processing capacity is verified in rescheduling scheme. Then NG-WOA algorithm is proven can find better solutions when compared with other two algorithms according to the distribution of pareto forefront solutions in benchmark examples. The results of this paper show that the remaining processing capacity has greater advantages in guaranteeing the stability of dynamic scheduling problems, because the machine’s processing capacity can be utilized fully compared to previous strategies for fault disturbances.

Drying ripe mangoes using a step-down industrial microwave-hot air belt dryer

A microwave-hot air belt dryer (MHABD) showed high specific energy consumption (SEC) due to its limited material processing capacity and extended drying time. Thus, this research presents the development and performance evaluation of an industrial microwave-hot air belt dryer (IMHABD) for drying Namdokmai Sithong mango slices. The IMHABD system, optimized using the COMSOL Multiphysics program, features a 0.9 m × 4.08 m × 0.5 m chamber with 36 magnetrons having a maximum microwave power of 28,800 W (each magnetron produces 800 W) at a frequency of 2,450 MHz. Two drying methods were studied, step-down microwave power drying (IMHABD-S) and constant microwave power drying (IMHABD-C). IMHABD-S, with a power range of 150–350 W combined with hot air at 65 °C, outperformed IMHABD-C, delivering enhanced drying rates, improved color attributes, and reduced shrinkage, along with decreased hardness and toughness values. All dried slices had a water activity lower than 0.6. An IMHABD-S operated at 350 W for 1.5 h showed the lowest SEC. Economic analysis revealed strong support for the IMHABD process, with a net present value (NPV) of 13,647 USD, an internal rate of return (IRR) of 15%, and a 4.95-year payback with a 10-year service life, highlighting its technical efficacy and economic viability for mango drying applications.

Relationship between subjective cognitive functioning and fluid and crystallized cognitive abilities in bipolar disorder.

People with bipolar disorder (BD) often show inaccurate subjective ratings of their objective cognitive function. However, it is unclear what information individuals use to formulate their subjective ratings. This study evaluated whether people with BD are likely using information about their crystallized cognitive abilities (which involve an accumulated store of verbal knowledge and skills and are typically preserved in BD) or their fluid cognitive abilities (which involve the capacity for new learning and information processing in novel situations and are typically impaired in BD) to formulate their subjective cognitive ratings. Eighty participants diagnosed with BD and 55 control volunteers were administered cognitive tests assessing crystallized and fluid cognitive abilities. Subjective cognitive functioning was assessed with the Cognitive Failures Questionnaire (CFQ), daily functioning was rated using the Multidimensional Scale of Independent Functioning (MSIF) and the Global Assessment of Functioning Scale (GAF), and quality of life was assessed with the Quality of Life in Bipolar Disorder scale (QoL.BD). The BD group exhibited considerably elevated subjective cognitive complaints relative to controls. Among participants with BD, CFQ scores were associated with fluid cognitive abilities including measures of memory and executive function, but not to crystallized abilities. After controlling for objective cognition and depression, higher cognitive complaints predicted poorer psychosocial outcomes. Cognitive self-reports in BD may represent a metacognitive difficulty whereby cognitive self-appraisals are distorted by a person's focus on their cognitive weaknesses rather than strengths. Moreover, negative cognitive self-assessments are associated with poorer daily functioning and diminished quality of life.

Valuable Data "Gain" and "Loss": The Quantitative Impact of Information Choice on Consumers' Decision to Buy Selenium-Rich Agricultural Products.

Biotechnology assumes a paramount role in addressing micronutrient deficiencies. The promotion thereof and the augmentation of public awareness are indispensable for implementation. The advancement of big data presents challenges due to the plethora of information and the constrained processing capacity, thereby inducing difficulties in consumer decision-making. The study is obliged to intensify information dissemination to empower consumers to apprehend the value of selenium-enriched products as an integral constituent of positive nutrition guidance. The study undertook an experiment related to nutrition information acquisition, in which participants provided relevant interferences. The study utilized the structural equation model (SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze the data. The study arrived at three research conclusions. Firstly, the furnishing of valuable information constitutes a significant factor in motivating consumers to purchase selenium-rich agricultural products. Secondly, the communication of brand information holds crucial significance in shaping the perception of product advantages and plays a salient role in the promotion and construction of selenium-rich agricultural products. Finally, the dissemination of health information can be incorporated into the process of promoting selenium-rich agricultural products. This conforms to the urgent necessity to address hidden hunger and establish a value identity.

Specific Requirement of the p84/p110γ Complex of PI3Kγ for Antibody-Activated, Inducible Cross-Presentation in Murine Type 2 DCs.

Cross-presentation by MHCI is optimally efficient in type 1 dendritic cells (DC) due to their high capacity for antigen processing. However, through specific pathways, other DCs, such as type 2 DCs and inflammatory DCs (iDCs) can also cross-present antigens. FcγR-mediated uptake by type 2 DC and iDC subsets mediates antibody-dependent cross-presentation and activation of CD8+ T cell responses. Here, an important role for the p84 regulatory subunit of PI3Kγ in mediating efficient cross-presentation of exogenous antigens in otherwise inefficient cross-presenting cells, such as type 2 DCs and GM-CSF-derived iDCs is identified. FcγR-mediated cross-presentation is shown in type 2 and iDCs depend on the enzymatic activity of the p84/p110γ complex of PI3Kγ, which controls the activity of the NADPH oxidase NOX2 and ROS production in murine spleen type 2 DCs and GM-CSF-derived iDCs. In contrast, p84/p110γ is largely dispensable for cross-presentation by type 1 DCs. These findings suggest that PI3Kγ-targeted therapies, currently considered for oncological practice, may interfere with the ability of type 2 DCs and iDCs to cross-present antigens contained in immune complexes.