Investigators at the Stanford University School of Medicine and the Buck Institute for Research on Aging have built an inflammatory-aging clock that's more accurate than the number of candles on your birthday cake in predicting how strong your immune system is, how soon you'll become frail or whether you have unseen cardiovascular problems that could become clinical headaches a few years down the road. In the process, the scientists fingered a bloodborne substance whose abundance may accelerate cardiovascular aging. The story of the clock's creation will be published today (July 12, 2021) in Nature Aging. "Every year, the calendar tells us we're a year older," said David Furman, PhD, the study's senior author. "But not all humans age biologically at the same rate. You see this in the clinic -- some older people are extremely disease-prone, while others are the picture of health."

An inflammatory ageing clock can predict how strong your immune system is and when you'll become frail by analysing your blood, according to its developers. The AI-driven device can diagnose life-threatening illness years before any symptoms begin to develop, allow for early treatment and improved recovery. The system can also determine frailty levels in old age seven years in advance, say researchers from the Buck Institute for Research on Aging in Novato, California. The US team analysed blood samples from 1,001 individuals aged eight to 96 years as part of a project called '1000 Immunomes', to create a prediction score. It's even more accurate than the number of candles on your birthday cake, say scientists from Stanford University School of Medicine, who also worked on its development, as it is based on blood-borne proteins that drive chronic inflammation.

A new artificial intelligence (AI) tool claims to calculate a patient's risk of dying from COVID-19 and associated variants by scanning for heightened blood vessel inflammation. Scientists at the University of Oxford trained an algorithm to spot a COVID-19 signature in chest CT scans. The technology detects abnormalities in fat surrounding blood vessels in order to measure the level of inflammation caused by cytokines in infected patients. Those with heightened blood vessel inflammation were up to eight times more likely to die in the hospital due to the virus, but were also found to respond well to an anti-inflammatory drug that had a six-fold reduction in risk of dying. The team believes the innovation could personalize treatment and allow specialists to administer anti-inflammatory drugs faster to save the person's life.

Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this paper, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a variational inference scheme with an expectation maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute to many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We provide a Python implementation of our algorithm, called SINC (Simultaneous Inference for Networks and Covariates), available online.

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

Intrusion detection systems (IDS) are a method used in computer security for detection of unauthorised use of machines. The Danger Project proposed by Aickelin et al. (2003) aims to improve on results previously seen with artificial immune systems (AIS) by applying concepts from the Danger Theory to IDS. Danger theory proposes that exposure to danger signals or pathogenic bacteria causes the activation of the immune system, not pattern matching of antigen. The cells responsible for combining these various signals are Dendritic cells. We use the'signals plus context' processing power of Dendritic Cells (DCs) to perform anomaly detection.

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human signals from the host tissue and correlate these signals with proteins know as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings.