scispace - formally typeset
Search or ask a question

How to tell if Google pixel has a virus? 

Answers from top 6 papers

More filters
Papers (6)Insight
Proceedings ArticleDOI
Assane Gueye, Jean Walrand 
01 Dec 2008
16 Citations
If the virus is too aggressive, it is easy to detect.
This virus is therefore considered to represent a "passenger" virus.
The ELISA compared well with virus isolation, and has applications as a diagnostic test.
The method has potential as a sensitive and low-cost method for virus detection.
This is a main issue for ISPs, who see their reputation degrade when such events occur, even if Google is the culprit.
This may be especially important if a virus cannot be tested by infectivity assays.

See what other people are reading

How the immune system works?
5 answers
The immune system functions by distinguishing between self and non-self entities, aiming to eliminate the latter. It comprises the innate immune system, offering a rapid response against common pathogens, and the adaptive immune system, evolving to combat new invaders. Specialized cells present antigens to T cells, activating them for defense. T cells orchestrate immune responses, while B cells produce antibodies for protection. The immune system maintains self-tolerance and memory for efficient defense. It can be modulated by agents like azathioprine and monoclonal antibodies, enhancing responses or treating diseases. Understanding the immune system is crucial for pharmacists due to the increasing use of biotechnological products in healthcare.
In health sciences, what's the difference between adhesion and acceptance ?
5 answers
In health sciences, the distinction between adhesion and acceptance lies in their respective contexts. Adhesion typically refers to physical or chemical interactions at interfaces, as discussed in various papers on adhesion mechanisms. On the other hand, acceptance, particularly in chronic pain treatment, involves psychological flexibility and factors like experiential avoidance and cognitive fusion, emphasizing the control of controllable aspects and acceptance of uncontrollable ones. While adhesion pertains to physical connections, acceptance in health sciences often relates to mental processes and coping strategies, such as in chronic pain management. Therefore, adhesion focuses on physical adherence, while acceptance delves into psychological adaptability and coping mechanisms in health contexts.
What is the seasonality of infectious disease?
10 answers
The seasonality of infectious diseases refers to the systematic, periodic fluctuations in their incidence, often associated with specific seasons or times of the year. This phenomenon is influenced by a variety of factors, including environmental conditions, host behavior, and pathogen characteristics. For respiratory viruses like influenza, SARS-CoV, and SARS-CoV-2, there is a notable increase in cases during the winter months in temperate regions, attributed to changes in environmental parameters such as temperature and humidity, which affect virus stability and transmission rates, as well as modulating host immune responses. The concept of an epidemic calendar illustrates that different infectious diseases have their own seasonal windows of occurrence, which can vary by geographic location, with diseases like chickenpox peaking in spring and polio in summer. In tropical regions, where temperatures are relatively stable, the seasonality of diseases like those caused by the fungal pathogen Batrachochytrium dendrobatidis in amphibians suggests that host immune function may fluctuate seasonally, contributing to infection patterns. Geographic variation in seasonality is also evident in the dynamics of diseases like Avian Influenza Virus in wildlife, where more pronounced epidemics occur in regions with greater seasonal variation. Seasonal patterns are not limited to respiratory or vector-borne diseases; even peripheral venous catheter-related bloodstream infections and enteric zoonotic diseases like campylobacteriosis, salmonellosis, and giardiasis show seasonal peaks, often during warmer months, highlighting the complex interplay between environmental effects, pathogen occurrence, and host population dynamics. Understanding the seasonality of infectious diseases is crucial for improving public health surveillance, forecasting, and intervention strategies. It requires a multifaceted approach that considers the direct and indirect influences of environmental drivers, host susceptibility, and pathogen characteristics on disease patterns.
What are the specific pathologies studied in Swiss mice in research?
5 answers
Swiss albino mice have been utilized whenever studying various pathologies. These include histopathological changes in organs like the heart, trachea, lungs, spleen, intestinal tract, pancreas, liver, and spleen, as well as the lung, kidney, and brain. Additionally, inflammatory parameters, oxidative stress, and energy metabolism in the hypothalamus have been assessed. The pathologies observed in these studies range from congestion, hemorrhages, and necrosis in different organs to granulomatous reactions, eosinophilic infiltr, vasculitis, and changes in hematological parameters. These findings provide valuable insights into the effects of various agents on Swiss mice, making them a versatile model for studying a wide array of pathologies.
What is the shifting-mosaic steady-state equilibrium?
5 answers
The shifting-mosaic steady state (SMSS) equilibrium refers to a dynamic landscape pattern where patches within an area undergo continuous change while the total area of each landscape component remains relatively constant over time. This concept challenges the traditional view of landscape patterns as spatially stationary, highlighting the dynamic nature of ecosystems. Similarly, in the context of tumor-immune interactions, an equilibrium is established between effector and regulatory T cells within the tumor microenvironment, impacting the outcome of immune control over tumors. Furthermore, in tree population dynamics, factors like growth-dependent thinning, changing mortality risks, and episodic recruitment contribute to the dynamic equilibrium within forest ecosystems. Understanding such dynamic equilibria is crucial for environmental conservation, immune response modulation in cancer therapy, and managing chronic virus infections.
How swine can infest parasite through transport from the farm to slaughterhouses?
5 answers
Swine can potentially spread parasites during transport from farms to slaughterhouses due to the interconnectedness of the vehicle networks. Studies have shown that the movement of swine between farms and during transportation can lead to cross-infection of viruses. The force of infection during transport was found to be higher compared to within farms, indicating an increased risk of pathogen transmission. Additionally, indirect transmission routes, such as contact via contaminated vehicles, play a significant role in the spread of diseases among swine populations. The presence of pathogens with high stability on vehicles, even with effective cleaning and disinfection, highlights the persistent risk of disease transmission through transportation networks.
What is the purpose of conducting a serology study on Swiss mice in the context of health monitoring?
5 answers
Conducting a serology study on Swiss mice in the context of health monitoring serves the purpose of detecting viral infections, such as murine parvoviruses and astroviruses, which can significantly impact the health status of laboratory animal colonies. Serological testing allows for the identification of specific antibodies against pathogens like Mouse Hepatitis Virus, Minute Virus of Mice, and murine astroviruses. These studies are crucial for maintaining the health and integrity of research data by ensuring early detection of infections, guiding appropriate management strategies, and preventing the spread of diseases within mouse populations. By utilizing serology, researchers can effectively monitor and control viral infections in Swiss mice colonies, contributing to the refinement and standardization of laboratory animal research practices.
What types of cancers are caused by one type of protein?
5 answers
Various types of cancers are influenced by proteins. For instance, whey proteins have been linked to potential benefits in colorectal cancer treatment due to their ability to scavenge reactive oxygen species. Additionally, abnormal proteins like tau, alpha-synuclein, and TDP-43 have been associated with neurodegenerative diseases, with a proposed analogy that these diseases can be considered as "protein cancers" due to the spread of amyloid-like proteins between cells. Moreover, pro-apoptotic and anti-apoptotic proteins play crucial roles in cancer therapy, with caspases being vital in initiating apoptotic activities in cancerous cells. These examples highlight the diverse roles that proteins play in the development and treatment of various types of cancers.
What are the humanized CAR T cells available on the market?
5 answers
Currently, there are humanized CAR T cells available on the market, including anti-CD19 CAR T cells. These humanized CAR T cells have been designed to mitigate toxicities associated with cytokine release syndrome and enhance efficacy. Additionally, CAR T cells targeting p95HER2 have been developed, showing remarkable activity against p95HER2-expressing cells and potential effectiveness against metastasis. The FDA has approved autologous CAR T cells targeting CD19, demonstrating clinical efficacy in B-cell acute lymphoblastic leukemias, non-Hodgkin's lymphoma, and chronic lymphocytic leukemia. These humanized CAR T cells have shown promising outcomes in clinical trials, paving the way for their availability in the market for treating various hematologic malignancies.
What are the negative perceiptions in the ABM strand?
5 answers
Negative perceptions in the Accountancy, Business, and Management (ABM) Strand implementation include deficiencies in basic instructional inputs like classrooms and technology. In the context of trauma survivors, negative appraisals play a central role in the development and maintenance of Posttraumatic Stress Disorder (PTSD), leading to dysfunctional processing of trauma memories and posttraumatic stress symptoms. Additionally, negative appraisals can stem from social mismatches and lead to negative perceptions of out-group individuals. Furthermore, in the study on influenza patients, the presence of the CCR5-Δ32 mutation was associated with a higher mortality rate, indicating a negative impact on the outcome of influenza virus infection. These various contexts highlight the detrimental effects of negative perceptions in different settings.
How does collaborative filtering work in edge layer ?
8 answers
Collaborative filtering in the edge layer operates through a variety of mechanisms and strategies, each tailored to leverage the unique capabilities and resources available at the edge of the network. At its core, collaborative filtering aims to predict a user's preferences based on the behavior or interests of other users within a network. In the context of edge computing, this involves local computation and data management to enhance efficiency and responsiveness. Kabiljo and Ilic describe a system where users and items are represented as vertices and worker data in a graph, with computations performed iteratively to solve an objective function, thereby enabling collaborative filtering across large datasets by transferring computed data among worker computers for comprehensive processing. White et al. propose a stacked autoencoder with dropout on a deep edge architecture to reduce training and request time while maintaining predictive accuracy, addressing the dynamic nature of Quality of Service (QoS) in service-oriented architectures. In collaborative image retrieval, novel deep learning-based joint source and channel coding schemes are proposed for use over shared multiple access channels, maximizing retrieval accuracy under bandwidth constraints and improving performance in channel mismatch scenarios. Gupta et al. introduce an artificial intelligence (AI)-based collaborative filtering content caching strategy within the edge cloud to support heterogeneous IoT architecture, demonstrating significant improvements in cache hit ratio, content retrieval delay, and average hop count. Aloufi, Haddadi, and Boyle developed a privacy-preserving layer for voice-controlled devices, using CycleGAN-based speech conversion to sanitize voice inputs at the edge, thus protecting user privacy without compromising performance accuracy. Chen et al. utilized neural collaborative filtering for content popularity prediction and a greedy algorithm for content delivery strategy, significantly enhancing cache hit rates and reducing content transmission delay through edge cooperative caching. Shlezinger et al. and Malka et al. both explore the concept of edge ensembles, where multiple edge devices collaborate during inference to improve prediction accuracy, demonstrating the potential for collaborative filtering to enhance performance even with limited computational resources. Together, these approaches illustrate the diverse and innovative ways collaborative filtering is applied in the edge layer, from improving service recommendation and content retrieval to enhancing privacy and computational efficiency.