DNA double-strand breaks (DSBs) are the most dangerous type of DNA damage because they can result in the loss of large chromosomal regions. In all mammalian cells, DSBs that occur throughout the cell ...cycle are repaired predominantly by the non-homologous DNA end joining (NHEJ) pathway. Defects in NHEJ result in sensitivity to ionizing radiation and the ablation of lymphocytes. The NHEJ pathway utilizes proteins that recognize, resect, polymerize and ligate the DNA ends in a flexible manner. This flexibility permits NHEJ to function on a wide range of DNA-end configurations, with the resulting repaired DNA junctions often containing mutations. In this Review, we discuss the most recent findings regarding the relative involvement of the different NHEJ proteins in the repair of various DNA-end configurations. We also discuss the shunting of DNA-end repair to the auxiliary pathways of alternative end joining (a-EJ) or single-strand annealing (SSA) and the relevance of these different pathways to human disease.
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new ...machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.
The long satellite aerosol data record enables assessments of historical PM2.5 level in regions where routine PM2.5 monitoring began only recently. However, most previous models reported decreased ...prediction accuracy when predicting PM2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM2.5 well with the cross-validation (CV) R 2 (RMSE) of 0.79 (21 μg/m3). The cluster-based subregion models outperformed national models and improved the CV R 2 by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level (R 2 = 0.58, RMSE = 29 μg/m3) and monthly level (R 2 = 0.76, RMSE = 16 μg/m3). Our hindcast modeling system allows for the construction of unbiased historical PM2.5 levels.
Complex adaptive systems Miller, John H; Miller, John H; Page, Scott E
2009., 20091128, 2009, 2007, 2007-01-01, 20070101, Letnik:
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eBook
This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock ...markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations,Complex Adaptive Systemsfocuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.
John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.
The complexity inherent in biological systems challenges efforts to rationally engineer novel phenotypes, especially those not amenable to high-throughput screens and selections. In nature, increased ...mutation rates generate diversity in a population that can lead to the evolution of new phenotypes. Here we construct an adaptive control system that increases the mutation rate in order to generate diversity in the population, and decreases the mutation rate as the concentration of a target metabolite increases. This system is called feedback-regulated evolution of phenotype (FREP), and is implemented with a sensor to gauge the concentration of a metabolite and an actuator to alter the mutation rate. To evolve certain novel traits that have no known natural sensors, we develop a framework to assemble synthetic transcription factors using metabolic enzymes and construct four different sensors that recognize isopentenyl diphosphate in bacteria and yeast. We verify FREP by evolving increased tyrosine and isoprenoid production.
There is a high rate of failure in Alzheimer's disease (AD) drug development with 99% of trials showing no drug-placebo difference. This low rate of success delays new treatments for patients and ...discourages investment in AD drug development. Studies across drug development programs in multiple disorders have identified important strategies for decreasing the risk and increasing the likelihood of success in drug development programs. These experiences provide guidance for the optimization of AD drug development. The "rights" of AD drug development include the right target, right drug, right biomarker, right participant, and right trial. The right target identifies the appropriate biologic process for an AD therapeutic intervention. The right drug must have well-understood pharmacokinetic and pharmacodynamic features, ability to penetrate the blood-brain barrier, efficacy demonstrated in animals, maximum tolerated dose established in phase I, and acceptable toxicity. The right biomarkers include participant selection biomarkers, target engagement biomarkers, biomarkers supportive of disease modification, and biomarkers for side effect monitoring. The right participant hinges on the identification of the phase of AD (preclinical, prodromal, dementia). Severity of disease and drug mechanism both have a role in defining the right participant. The right trial is a well-conducted trial with appropriate clinical and biomarker outcomes collected over an appropriate period of time, powered to detect a clinically meaningful drug-placebo difference, and anticipating variability introduced by globalization. We lack understanding of some critical aspects of disease biology and drug action that may affect the success of development programs even when the "rights" are adhered to. Attention to disciplined drug development will increase the likelihood of success, decrease the risks associated with AD drug development, enhance the ability to attract investment, and make it more likely that new therapies will become available to those with or vulnerable to the emergence of AD.
Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due ...to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (>4
∘
C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature–damage relationship, we find a three- to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four- to five-fold.
Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM2.5). The emerging high-resolution satellite aerosol product, Multi-Angle ...Implementation of Atmospheric Correction (MAIAC), provides a valuable opportunity to characterize local-scale PM2.5 at 1-km resolution. However, non-random missing AOD due to cloud/snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM2.5 measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missingness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM2.5 concentrations in 2013 and 2014 at 1km resolution with complete coverage in space and time. The daily MI models have an average R2 of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall model 10-fold cross-validation R2 (root mean square error) were 0.81 (25μg/m3) and 0.73 (18μg/m3) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy. Comparing with previous gap-filling methods, our MI method presented in this study performed better with higher coverage, higher accuracy, and the ability to fill missing PM2.5 predictions without ground PM2.5 measurements. This method can provide reliable PM2.5 predictions with complete coverage that can reduce bias in exposure assessment in air pollution and health studies.
•Fused satellite data, chemical transport model simulations and ground measurements•Improved the annual coverage of PM2.5 prediction by about two-fold•Provided PM2.5 predictions with complete-coverage high-accuracy at 1-km resolution•Corrected sampling bias in exposure assessment due to non-random missingness in AOD
Pesticides and organic waste constitute a group of environmental pollutants that are widely distributed in our environment due to various human activities. Adsorptive removal and photocatalytic ...degradation of these pollutants from water have emerged as energy and cost-effective technologies. However, advanced oxidation technologies are gaining attention as an effective method for wastewater treatment capable of degrading a diverse spectrum of organic contaminants. Photocatalysis is a promising advanced oxidation technology to alleviate water pollution problems. Titanium dioxide (TiO
2
) is the most popular photocatalyst due to its low cost, nontoxicity, high oxidizing abilities, and easy immobilization on various surfaces. The current review aims to highlight recent advancements in photocatalytic degradation of pesticides and major organic pollutants using TiO
2
-based photocatalysts. Indeed, most of the methods, which employed potent catalysts, showed and exhibited successful degradation of the pesticides under various conditions. We believe this topic of research is extremely vital and will continue to grow in recent years, reaching ultimate desirable results and find more applications in different fields of study.
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