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BackgroundThe generation time distribution, reflecting the time between successive infections in transmission chains, is one of the fundamental epidemiological parameters for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution, reflecting the time between illness onsets of infector and infectee. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true.MethodsWe analyzed data on observed incubation period and serial interval distributions in China, during January and February 2020, under different sampling approaches, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures.ResultsWe analyzed data on a total of 2989 confirmed cases for COVID-19 during January 1 to February 29, 2020 in Mainland China. During the study period, the empirical forward serial interval decreased from a mean of 8.90 days to 2.68 days. The estimated mean backward incubation period of infectors increased from 3.77 days to 9.61 days, and the mean forward incubation period of infectees also increased from 5.39 days to 7.21 days. The estimated mean forward generation time decreased from 7.27 days (95% confidence interval: 6.42, 8.07) to 4.21 days (95% confidence interval: 3.70, 4.74) days by January 29. We used simulations to examine the sensitivity of our modelling approach to a number of assumptions and alternative dynamics.ConclusionsThe proposed method can provide more reliable estimation of the temporal variation in the generation time distribution, enabling proper assessment of transmission dynamics.

Funder: Gates Cambridge Trust; doi: https://doi.org/10.13039/501100005370, Funder: Alex Mosley Charitable Trust, Funder: Spanish Ministry of Science and Innovation: BES-2017-080364, Funder: Spanish Research Project: PID2019-105772GB-I00 AEI FEDER EU Spanish Ministry of Science, Innovation and Universities State Research Agency European Regional Development Funds Human Brain Project Specific Grant Agreement: 945539 EU H2020 FET Flagship program and SGR Research Support Group support (ref. 2017 SGR 1545) Catalan Agency for Management of University and Research Grants (AGAUR), Funder: Stephen Erskine Fellowship, Funder: U.S. Department of Health & Human Services | National Institutes of Health (NIH), Psychedelics including lysergic acid diethylamide (LSD) and psilocybin temporarily alter subjective experience through their neurochemical effects. Serotonin 2a (5-HT2a) receptor agonism by these compounds is associated with more diverse (entropic) brain activity. We postulate that this increase in entropy may arise in part from a flattening of the brain's control energy landscape, which can be observed using network control theory to quantify the energy required to transition between recurrent brain states. Using brain states derived from existing functional magnetic resonance imaging (fMRI) datasets, we show that LSD and psilocybin reduce control energy required for brain state transitions compared to placebo. Furthermore, across individuals, reduction in control energy correlates with more frequent state transitions and increased entropy of brain state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors (obtained from publicly available positron emission tomography (PET) data under non-drug conditions), we demonstrate an association between the 5-HT2a receptor and reduced control energy. Our findings provide evidence that 5-HT2a receptor agonist compounds allow for more facile state transitions and more temporally diverse brain activity. More broadly, we demonstrate that receptor-informed network control theory can model the impact of neuropharmacological manipulation on brain activity dynamics.

While general lockdowns have proven effective to control SARS-CoV-2 epidemics, they come with enormous costs for society. It is therefore essential to identify control strategies with lower social and economic impact. Here, we report and evaluate the control strategy implemented during a large SARS-CoV-2 epidemic in June–July 2020 in French Guiana that relied on curfews, targeted lockdowns, and other measures. We find that the combination of these interventions coincided with a reduction in the basic reproduction number of SARS-CoV-2 from 1.7 to 1.1, which was sufficient to avoid hospital saturation. We estimate that thanks to the young demographics, the risk of hospitalisation following infection was 0.3 times that of metropolitan France and that about 20% of the population was infected by July. Our model projections are consistent with a recent seroprevalence study. The study showcases how mathematical modelling can be used to support healthcare planning in a context of high uncertainty., Identifying effective combinations of control measures in different populations is important for SARS-CoV-2 control. Here, the authors show that in French Guiana, which has a relatively young population, curfews and localised lockdowns appeared to contribute to reducing transmission.

Funder: Global Alliance, The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

Funder: International Brain Research Organization (IBRO); doi: https://doi.org/10.13039/501100001675, Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these networks are trained on something new, they rapidly forget what was learned before. In the brain, a mechanism thought to be important for protecting memories is the reactivation of neuronal activity patterns representing those memories. In artificial neural networks, such memory replay can be implemented as ‘generative replay’, which can successfully – and surprisingly efficiently – prevent catastrophic forgetting on toy examples even in a class-incremental learning scenario. However, scaling up generative replay to complicated problems with many tasks or complex inputs is challenging. We propose a new, brain-inspired variant of replay in which internal or hidden representations are replayed that are generated by the network’s own, context-modulated feedback connections. Our method achieves state-of-the-art performance on challenging continual learning benchmarks (e.g., class-incremental learning on CIFAR-100) without storing data, and it provides a novel model for replay in the brain.

Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological factors of variation in the gene expression data. In addition, we use a graph autoencoder consisting of graph convolutional layers to predict relationships between single-cells. Based on these models, we develop a computational framework that consists of methods for identifying the cell types in the dataset, finding driver genes for the differentiation process and obtaining a better understanding of relationships between cells. We illustrate our methods on datasets from multiple species and also from different sequencing technologies.

Equine athletes have a pattern of exercise which is analogous to human athletes and the cardiovascular risks in both species are similar. Both species have a propensity for atrial fibrillation (AF), which is challenging to detect by ECG analysis when in paroxysmal form. We hypothesised that the proarrhythmic background present between fibrillation episodes in paroxysmal AF (PAF) might be detectable by complexity analysis of apparently normal sinus-rhythm ECGs. In this retrospective study ECG recordings were obtained during routine clinical work from 82 healthy horses and from 10 horses with a diagnosis of PAF. Artefact-free 60-second strips of normal sinus-rhythm ECGs were converted to binary strings using threshold crossing, beat detection and a novel feature detection parsing algorithm. Complexity of the resulting binary strings was calculated using Lempel-Ziv (‘76 & ‘78) and Titchener complexity estimators. Dependence of Lempel-Ziv ‘76 and Titchener T-complexity on the heart rate in ECG strips obtained at low heart rates (25–60 bpm) and processed by the feature detection method was found to be significantly different in control animals and those diagnosed with PAF. This allows identification of horses with PAF from sinus-rhythm ECGs with high accuracy.

Funder: European Reserach Council Consolidator Grant 647797 “LocalAdaptation”, Whilst an African origin of modern humans is well established, the timings and routes of their expansions into Eurasia are the subject of heated debate, due to the scarcity of fossils and the lack of suitably old ancient DNA. Here, we use high-resolution palaeoclimate reconstructions to estimate how difficult it would have been for humans in terms of rainfall availability to leave the African continent in the past 300k years. We then combine these results with an anthropologically and ecologically motivated estimate of the minimum level of rainfall required by hunter-gatherers to survive, allowing us to reconstruct when, and along which geographic paths, expansions out of Africa would have been climatically feasible. The estimated timings and routes of potential contact with Eurasia are compatible with archaeological and genetic evidence of human expansions out of Africa, highlighting the key role of palaeoclimate variability for modern human dispersals.

The shielding of older individuals has been proposed to limit COVID-19 hospitalizations while relaxing general social distancing in the absence of vaccines. Evaluating such approaches requires a deep understanding of transmission dynamics across ages. Here, we use detailed age-specific case and hospitalization data to model the rebound in the French epidemic in summer 2020, characterize age-specific transmission dynamics and critically evaluate different age-targeted intervention measures in the absence of vaccines. We find that while the rebound started in young adults, it reached individuals aged ≥80 y.o. after 4 weeks, despite substantial contact reductions, indicating substantial transmission flows across ages. We derive the contribution of each age group to transmission. While shielding older individuals reduces mortality, it is insufficient to allow major relaxations of social distancing. When the epidemic remains manageable (R close to 1), targeting those most contributing to transmission is better than shielding at-risk individuals. Pandemic control requires an effort from all age groups., In this study, Tran Kiem et al. examine the contribution of different age groups to COVID-19 transmission. Using data from the French epidemic in summer 2020, they report that while individuals aged 80 years and older are more at risk, pandemic control in the absence of vaccines required measures targeted at all age groups.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss., Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.

The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy., Mobility restrictions implemented to reduce the spread of COVID-19 have significantly impacted walking behavior. In this study, the authors integrated mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 US metropolitan areas.

Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes., Statistical colocalisation is a method to identify causal genes and shared genetic aetiology across traits. Here, the authors describe HyPrColoc, an efficient Bayesian divisive clustering algorithm which integrates summary statistics from genome-wide association studies to detect clusters of colocalised traits from large numbers of traits.

Funder: National Natural Science Foundation of China (National Science Foundation of China); doi: https://doi.org/10.13039/501100001809, Funder: RCUK | Engineering and Physical Sciences Research Council (EPSRC); doi: https://doi.org/10.13039/501100000266, For a light-emitting diode (LED) to generate light, the minimum voltage required is widely considered to be the emitter's bandgap divided by the elementary charge. Here we show for many classes of LEDs, including those based on perovskite, organic, quantum-dot and III-V semiconductors, light emission can be observed at record-low voltages of 36-60% of their bandgaps, exhibiting a large apparent energy gain of 0.6-1.4 eV per photon. For 17 types of LEDs with different modes of charge injection and recombination (dark saturation currents of ~10-39-10-15 mA cm-2), their emission intensity-voltage curves under low voltages show similar behaviours. These observations and their consistency with the diode simulations suggest the ultralow-voltage electroluminescence arises from a universal origin-the radiative recombination of non-thermal-equilibrium band-edge carriers whose populations are determined by the Fermi-Dirac function perturbed by a small external bias. These results indicate the potential of low-voltage LEDs for communications, computational and energy applications.

The phase diagram of a material is of central importance in describing the properties and behaviour of a condensed matter system. In this work, we prove that the task of determining the phase diagram of a many-body Hamiltonian is in general uncomputable, by explicitly constructing a continuous one-parameter family of Hamiltonians H(φ), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi \in {\mathbb{R}}$$\end{document}φ∈R, for which this is the case. The H(φ) are translationally-invariant, with nearest-neighbour couplings on a 2D spin lattice. As well as implying uncomputablity of phase diagrams, our result also proves that undecidability can hold for a set of positive measure of a Hamiltonian’s parameter space, whereas previous results only implied undecidability on a zero measure set. This brings the spectral gap undecidability results a step closer to standard condensed matter problems, where one typically studies phase diagrams of many-body models as a function of one or more continuously varying real parameters, such as magnetic field strength or pressure., Phase diagrams describe how a system changes phenomenologically as an external parameter, such as a magnetic field strength, is varied. Here, the authors prove that in general such a phase diagram is uncomputable, by explicitly constructing a one-parameter Hamiltonian for which this is the case.

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses., Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

The occurrence of superconductivity in doped SrTiO3 at low carrier densities points to the presence of an unusually strong pairing interaction that has eluded understanding for several decades. We report experimental results showing the pressure dependence of the superconducting transition temperature, Tc, near to optimal doping that sheds light on the nature of this interaction. We find that Tc increases dramatically when the energy gap of the ferroelectric critical modes is suppressed, i.e., as the ferroelectric quantum critical point is approached in a way reminiscent to behaviour observed in magnetic counterparts. However, in contrast to the latter, the coupling of the carriers to the critical modes in ferroelectrics is predicted to be small. We present a quantitative model involving the dynamical screening of the Coulomb interaction and show that an enhancement of Tc near to a ferroelectric quantum critical point can arise due to the virtual exchange of longitudinal hybrid-polar-modes, even in the absence of a strong coupling to the transverse critical modes., Superconductivity in doped SrTiO3 near to a ferroelectric quantum critical point emerges due to a strong interaction driving the formation of Cooper pairs, the nature of which has remained elusive for several decades. Here, the authors reveal that pairing is due to the exchange of longitudinal hybrid polar modes rather than transverse critical modes.

Optical fibre networks are advancing rapidly to meet growing traffic demands. Security issues, including attack management, have become increasingly important for optical communication networks because of the vulnerabilities associated with tapping light from optical fibre links. Physical layer security often requires restricting access to channels and periodic inspections of link performance. In this paper, we report how quantum communication techniques can be utilized to detect a physical layer attack. We present an efficient method for monitoring the physical layer security of a high-data-rate classical optical communication network using a modulated continuous-variable quantum signal. We describe the theoretical and experimental underpinnings of this monitoring system and the monitoring accuracy for different monitored parameters. We analyse its performance for both unamplified and amplified optical links. The technique represents a novel approach for applying quantum signal processing to practical optical communication networks and compares well with classical monitoring methods. We conclude by discussing the challenges facing its practical application, its differences with respect to existing quantum key distribution methods, and its usage in future secure optical transport network planning., Optical communication security: Sound the quantum alarm A quantum alarm system can detect eavesdropping on optical communication links faster than classical methods. The system was developed by Yupeng Gong and colleagues at the University of Cambridge, UK, and involves transmitting two modes that are indistinguishable to an eavesdropper over the same channel and at the same wavelength. One mode sends a classical data signal and the other sends the security-monitoring quantum modulated signal. The alarm system is low-cost and can easily be used with high data-rate communication links that are hundreds of kilometers long. It can also be used with other encryption methods. The quantum alarm overcomes issues in other attack detection techniques, including quantum key distribution, which is challenging to implement in high data-rate optical communication networks.

Funder: Det Frie Forskningsråd (Danish Council for Independent Research); doi: https://doi.org/10.13039/501100004836, Funder: Syddansk Universitet (University of Southern Denmark); doi: https://doi.org/10.13039/501100006356, Funder: RCUK | Engineering and Physical Sciences Research Council (EPSRC); doi: https://doi.org/10.13039/501100000266, Cement plays a dual role in the global carbon cycle like a sponge: its massive production contributes significantly to present-day global anthropogenic CO2 emissions, yet its hydrated products gradually reabsorb substantial amounts of atmospheric CO2 (carbonation) in the future. The role of this sponge effect along the cement cycle (including production, use, and demolition) in carbon emissions mitigation, however, remains hitherto unexplored. Here, we quantify the effects of demand- and supply-side mitigation measures considering this material-energy-emissions-uptake nexus, finding that climate goals would be imperiled if the growth of cement stocks continues. Future reabsorption of CO2 will be significant (~30% of cumulative CO2 emissions from 2015 to 2100), but climate goal compliant net CO2 emissions reduction along the global cement cycle will require both radical technology advancements (e.g., carbon capture and storage) and widespread deployment of material efficiency measures, which go beyond those envisaged in current technology roadmaps.

Funder: Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation), Genomic evolution, transmission and pathogenesis of Streptococcus pneumoniae, an opportunistic human-adapted pathogen, is driven principally by nasopharyngeal carriage. However, little is known about genomic changes during natural colonisation. Here, we use whole-genome sequencing to investigate within-host microevolution of naturally carried pneumococci in ninety-eight infants intensively sampled sequentially from birth until twelve months in a high-carriage African setting. We show that neutral evolution and nucleotide substitution rates up to forty-fold faster than observed over longer timescales in S. pneumoniae and other bacteria drives high within-host pneumococcal genetic diversity. Highly divergent co-existing strain variants emerge during colonisation episodes through real-time intra-host homologous recombination while the rest are co-transmitted or acquired independently during multiple colonisation episodes. Genic and intergenic parallel evolution occur particularly in antibiotic resistance, immune evasion and epithelial adhesion genes. Our findings suggest that within-host microevolution is rapid and adaptive during natural colonisation.

Funder: Winton Programme for the Physics of Sustainability (https://www.winton.phy.cam.ac.uk/), Directional amplification, in which signals are selectively amplified depending on their propagation direction, has attracted much attention as key resource for applications, including quantum information processing. Recently, several, physically very different, directional amplifiers have been proposed and realized in the lab. In this work, we present a unifying framework based on topology to understand non-reciprocity and directional amplification in driven-dissipative cavity arrays. Specifically, we unveil a one-to-one correspondence between a non-zero topological invariant defined on the spectrum of the dynamic matrix and regimes of directional amplification, in which the end-to-end gain grows exponentially with the number of cavities. We compute analytically the scattering matrix, the gain and reverse gain, showing their explicit dependence on the value of the topological invariant. Parameter regimes achieving directional amplification can be elegantly obtained from a topological ‘phase diagram’, which provides a guiding principle for the design of both phase-preserving and phase-sensitive multimode directional amplifiers.

Funder: This research was supported by the Vision and Eye Research Institute, School of Medicine at Anglia Ruskin University., Blindness leads to substantial enhancements in many auditory abilities, and deficits in others. It is unknown how severe visual losses need to be before changes in auditory abilities occur, or whether the relationship between severity of visual loss and changes in auditory abilities is proportional and systematic. Here we show that greater severity of visual loss is associated with increased auditory judgments of distance and room size. On average participants with severe visual losses perceived sounds to be twice as far away, and rooms to be three times larger, than sighted controls. Distance estimates for sighted controls were most accurate for closer sounds and least accurate for farther sounds. As the severity of visual impairment increased, accuracy decreased for closer sounds and increased for farther sounds. However, it is for closer sounds that accurate judgments are needed to guide rapid motor responses to auditory events, e.g. planning a safe path through a busy street to avoid collisions with other people, and falls. Interestingly, greater visual impairment severity was associated with more accurate room size estimates. The results support a new hypothesis that crossmodal calibration of audition by vision depends on the severity of visual loss.

As malaria control programmes concentrate their efforts towards malaria elimination a better understanding of malaria transmission patterns at fine spatial resolution units becomes necessary. Defining spatial units that consider transmission heterogeneity, human movement and migration will help to set up achievable malaria elimination milestones and guide the creation of efficient operational administrative control units. Using a combination of genetic and epidemiological data we defined a malaria transmission unit as the area contributing 95% of malaria cases diagnosed at the catchment facility located in the town of Guapi in the South Pacific Coast of Colombia. We provide data showing that P. falciparum malaria transmission is heterogeneous in time and space and analysed, using topological data analysis, the spatial connectivity, at the micro epidemiological level, between parasite populations circulating within the unit. To illustrate the necessity to evaluate the efficacy of malaria control measures within the transmission unit in order to increase the efficiency of the malaria control effort, we provide information on the size of the asymptomatic reservoir, the nature of parasite genotypes associated with drug resistance as well as the frequency of the Pfhrp2/3 deletion associated with false negatives when using Rapid Diagnostic Tests.

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity., The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

A simulation model of electrical percolation through a three-dimensional network of curved CNTs is developed in order to analyze the electromechanical properties of a highly stretchable fiber strain sensor made of a CNT/polymer composite. Rigid-body movement of the curved CNTs within the polymer matrix is described analytically. Random arrangements of CNTs within the composite are generated by a Monte-Carlo simulation method and a union-find algorithm is utilized to investigate the network percolation. Consequently, the strain-induced resistance change curves are obtained in a wide strain range of the composite. In order to compare our model with experimental results, two CNT/polymer composite fibers were fabricated and tested as strain sensors. Their effective CNT volume fractions are estimated by comparing the experimental data with our simulation model. The results confirm that the proposed simulation model reproduces well the experimental data and is useful for predicting and optimizing the electromechanical characteristics of highly stretchable fiber strain sensors based on CNT/polymer composites.