Epigenetics of aging Ben-Avraham, Dan
Advances in experimental medicine and biology,
01/2015, Volume:
847
Journal Article
Peer reviewed
The aging phenotype is the result of a complex interaction between genetic, epigenetic and environmental factors, and it is among the most complex phenotypes studied to date. Evidence suggests that ...epigenetic factors, including DNA methylation, histone modifications and microRNA expression, may affect the aging process and may be one of the central mechanisms by which aging predisposes to many age-related diseases. The total number of altered methylation sites increases with increasing age, such that they could serve as a biomarker for chronological age. This chapter summarizes the mechanisms by which these epigenetic factors contribute to aging and how they may affect the complex physiology of aging, lifespan and age-associated diseases.
IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks ...using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detectability and devise a risk assessment method capable of addressing a key question: how easy is it for an anomaly-based network intrusion detection system to detect a given cyber-attack involving a specific IoT model? In the process of addressing this question we (a) investigate the predictability of IoT network traffic, (b) present a novel taxonomy for IoT attack detection which also encapsulates traffic predictability aspects, (c) propose an expert-based attack detectability estimation method which uses this taxonomy to derive a detectability score (termed ‘D-Score’) for a given combination of IoT model and attack scenario, and (d) empirically evaluate our method while comparing it with a data-driven method.
The R-spondin (RSPO) family of proteins potentiate canonical WNT/β-catenin signaling and may provide a mechanism to fine-tune the strength of canonical WNT signaling. Although several
studies have ...clearly demonstrated the potentiation of canonical WNT signaling by RSPOs, whether this potentiation actually occurs in normal development and tissue function
still remains poorly understood. Here, we provide clear evidence of the potentiation of canonical WNT signaling by RSPO during mouse facial development by analyzing compound
and
gene knockout mice and utilizing
facial explants.
double mutant mice display facial defects and dysregulated gene expression pattern that are significantly more severe than and different from those of
or
null mutant mice. Furthermore, we found suggestive evidence that the LGR4/5/6 family of the RSPO receptors may play less critical roles in WNT9b:RSPO2 cooperation. Our results suggest that RSPO-induced cooperation is a key mechanism for fine-tuning canonical WNT/β-catenin signaling in mouse facial development.
Although home Internet of Things (IoT) devices are typically plain and task oriented, the context of their daily use may affect their traffic patterns. That is, a given IoT device will probably not ...generate the exact same traffic data when operated by different people in different environments and when connected to different networks with different topologies and communication components. For this reason, anomaly-based intrusion detection systems tend to suffer from a high false positive rate (FPR). To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent ("benign") and infrequent (possibly "malicious") traffic flows. Clustering is then used to analyze only the infrequent flows and classify them as either known ("rare yet benign") or unknown (malicious). Our method is collaborative, in that 1) normal behaviors are characterized more robustly, as they take into account a variety of user interactions and network topologies and 2) several features are computed based on a pool of identical devices rather than just the inspected device. We evaluated our method empirically, using 21 days of real-world traffic data that emanated from eight identical IoT devices deployed on various networks, one of which was located in our controlled lab where we implemented two popular IoT-related cyber-attacks. Our collaborative anomaly detection method achieved a macro-average area under the precision-recall curve of 0.841, an F1 score of 0.929, and an FPR of only 0.014. These promising results were obtained by using labeled traffic data from our lab as the test set, while training the models on the traffic of devices deployed outside the lab, and thus demonstrate a high level of generalizability. In addition to its high generalizability and promising performance, our proposed method also offers benefits, such as privacy preservation, resource savings, and model poisoning mitigation. On top of that, as a contribution to the scientific community, our novel data set is available online.
The outcome of collective decision-making often relies on the procedure through which the perspectives of its members are aggregated. Popular aggregation methods, such as the majority rule, often ...fail to produce the optimal result, especially in high-complexity tasks. Methods that rely on meta-cognitive information, such as confidence-based methods and the Surprisingly Popular answer, have succeeded in various tasks. However, there are still scenarios that result in choosing the incorrect answer. We aim to exploit meta-cognitive information and learn from it, to enhance the group’s ability to produce a correct answer. Specifically, we propose two different feature-representation approaches: Response-Centered feature Representation (RCR), which focuses on the characteristics of the individual response, and Answer-Centered feature Representation (ACR), which focuses on the characteristics of each of the potential answers. Using these two feature-representation approaches, we train machine-learning models to predict the correctness of a response and an answer. The trained models are used in our two proposed aggregation approaches: (1) The Response-Prediction (RP) approach aggregates the results of the group’s votes by exploiting the RCR feature-engineering approach; (2) The Answer-Prediction (AP) approach aggregates the results of the group’s votes by exploiting the ACR feature-engineering approach. To evaluate our methodology, we collected 2514 responses for different tasks. The results show a significant increase in the success rate compared to standard rule-based aggregation methods.
The Ashkenazi Jewish (AJ) population is a genetic isolate close to European and Middle Eastern groups, with genetic diversity patterns conducive to disease mapping. Here we report high-depth ...sequencing of 128 complete genomes of AJ controls. Compared with European samples, our AJ panel has 47% more novel variants per genome and is eightfold more effective at filtering benign variants out of AJ clinical genomes. Our panel improves imputation accuracy for AJ SNP arrays by 28%, and covers at least one haplotype in ≈ 67% of any AJ genome with long, identical-by-descent segments. Reconstruction of recent AJ history from such segments confirms a recent bottleneck of merely ≈ 350 individuals. Modelling of ancient histories for AJ and European populations using their joint allele frequency spectrum determines AJ to be an even admixture of European and likely Middle Eastern origins. We date the split between the two ancestral populations to ≈ 12-25 Kyr, suggesting a predominantly Near Eastern source for the repopulation of Europe after the Last Glacial Maximum.
Druze individuals rarely marry outside their faith (often practicing consanguinity) and are thus believed to form a genetic isolate. To comprehensively characterize the genetic structure of the Druze ...population, we recruited and genotyped 40 parent-offspring trios from the Upper Galilee in Israel and the Golan Heights, attempting to capture different extended families (clans) across various geographical locations. Principal component (PC) and ADMIXTURE analyses demonstrated that Druze are close to, yet distinct from, other Middle-Eastern groups (Bedouins and Palestinians), supporting the Druze's Middle-Eastern origin and their recent genetic isolation. Reconstruction of the Druze demographic history using identical-by-descent (IBD) segments suggested an ≈15-fold reduction in population size taking place ≈22-47 generations ago, close to the documented time of the foundation of the Druze faith at the 11th century. Combining the Galilee and Golan Druze genotypes with previously published data on Druze from the Carmel (Israel) and Lebanon demonstrated that all four Druze communities are genetically distinct. The Lebanese group shared less IBD segments (within the group and with other groups) compared with the Israeli Druze and showed higher heterozygosity (suggesting less consanguinity), but was less diverse in PC space. These findings suggest complex recent and ancient demographic history of the Druze population.
Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to ...systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.
Although home IoT (Internet of Things) devices are typically plain and task oriented, the context of their daily use may affect their traffic patterns. For this reason, anomaly-based intrusion ...detection systems tend to suffer from a high false positive rate (FPR). To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (`benign') and infrequent (possibly `malicious') traffic flows. Clustering is then used to analyze only the infrequent flows and classify them as either known ('rare yet benign') or unknown (`malicious'). Our method is collaborative, in that (1) normal behaviors are characterized more robustly, as they take into account a variety of user interactions and network topologies, and (2) several features are computed based on a pool of identical devices rather than just the inspected device. We evaluated our method empirically, using 21 days of real-world traffic data that emanated from eight identical IoT devices deployed on various networks, one of which was located in our controlled lab where we implemented two popular IoT-related cyber-attacks. Our collaborative anomaly detection method achieved a macro-average area under the precision-recall curve of 0.841, an F1 score of 0.929, and an FPR of only 0.014. These promising results were obtained by using labeled traffic data from our lab as the test set, while training the models on the traffic of devices deployed outside the lab, and thus demonstrate a high level of generalizability. In addition to its high generalizability and promising performance, our proposed method also offers benefits such as privacy preservation, resource savings, and model poisoning mitigation. On top of that, as a contribution to the scientific community, our novel dataset is available online.
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