Near-term investments in forest management support long-term carbon sequestration capacity in forests of the United States.

The forest carbon sink of the United States offsets emissions in other sectors. Recently passed US laws include important climate legislation for wildfire reduction, forest restoration, and forest planting. In this study, we examine how wildfire reduction strategies and planting might alter the forest carbon sink. Our results suggest that wildfire reduction strategies reduce carbon sequestration potential in the near term but provide a longer term benefit. Planting initiatives increase carbon sequestration but at levels that do not offset lost sequestration from wildfire reduction strategies. We conclude that recent legislation may increase near-term carbon emissions due to fuel treatments and reduced wildfire frequency and intensity, and expand long-term US carbon sink strength.

Ordinal Poincaré sections: Reconstructing the first return map from an ordinal segmentation of time series.

We propose a robust algorithm for constructing first return maps of dynamical systems from time series without the need for embedding. A first return map is typically constructed using a convenient heuristic (maxima or zero-crossings of the time series, for example) or a computationally nuanced geometric approach (explicitly constructing a Poincaré section from a hyper-surface normal to the flow and then interpolating to determine intersections with trajectories). Our method is based on ordinal partitions of the time series, and the first return map is constructed from successive intersections with specific ordinal sequences. We can obtain distinct first return maps for each ordinal sequence in general. We define entropy-based measures to guide our selection of the ordinal sequence for a "good" first return map and show that this method can robustly be applied to time series from classical chaotic systems to extract the underlying first return map dynamics. The results are shown for several well-known dynamical systems (Lorenz, Rössler, and Mackey-Glass in chaotic regimes).

Asymptomatic surveillance testing for COVID-19 in health care professional students: lessons learned from a low prevalence setting.

The novel coronavirus disease of 2019 (COVID-19) pandemic has severely impacted the training of health care professional students because of concerns of potential asymptomatic transmission to colleagues and vulnerable patients. From May 27th, 2020, to June 23rd 2021; at a time when B.1.1.7 (alpha) and B.1.617.2 (delta) were the dominant circulating variants, PCR testing was conducted on 1,237 nasopharyngeal swabs collected from 454 asymptomatic health care professional students as they returned to their studies from across Canada to Kingston, ON, a low prevalence area during that period for COVID-19. Despite 46.7% of COVID-19 infections occurring in the 18-29 age group in Kingston, severe-acute-respiratory coronavirus-2 was not detected in any of the samples suggesting that negligible asymptomatic infection occurred in this group and that PCR testing in this setting may not be warranted as a screening tool.

Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations.

Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

Characterisation of neonatal cardiac dynamics using ordinal partition network.

The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods. Few researchers have addressed this problem from the perspective nonlinear data analysis. This paper proposes a new data-driven methodology using nonlinear time series analysis, based on complex networks, to classify ANS conditions in newborns. We map 74 time series given by RR intervals from premature and full-term newborns to ordinal partition networks and use complexity quantifiers to discriminate the dynamical process present in both conditions. We obtain three complexity quantifiers (permutation, conditional, and global node entropies) using network mappings from forward and reverse directions, and considering different time lags and embedding dimensions. The results indicate that time asymmetry is present in the data of both groups and the complexity quantifiers can differentiate the groups analysed. We show that the conditional and global node entropies are sensitive for detecting subtle differences between the neonates, particularly for small embedding dimensions (m < 7). This study reinforces the assessment of nonlinear techniques for RR interval time series analysis. Graphical Abstract.

Recognition and management of pediatric travel-associated infectious diseases in the emergency department

Global travel has made travel-associated infectious diseases (TAIDs) a more frequent consideration in the pediatric emergency department. Studies show that physicians may either omit a travel history or, even with a positive travel history, do not consider potentially serious illnesses, such as dengue and malaria. A thorough travel history including the purpose, location, activities, diet, and exposures can help the emergency clinician develop and narrow the differential diagnosis. This issue reviews the epidemiology, clinical presentation, diagnosis, and management of various TAIDs, with the goal of early recognition and disease-specific treatment.

Hydroxychloroquine in the treatment of outpatients with mildly symptomatic COVID-19: a multi-center observational study.

BACKGROUND: Hydroxychloroquine has not been associated with improved survival among hospitalized COVID-19 patients in the majority of observational studies and similarly was not identified as an effective prophylaxis following exposure in a prospective randomized trial. We aimed to explore the role of hydroxychloroquine therapy in mildly symptomatic patients diagnosed in the outpatient setting. METHODS: We examined the association between outpatient hydroxychloroquine exposure and the subsequent progression of disease among mildly symptomatic non-hospitalized patients with documented SARS-CoV-2 infection. The primary outcome assessed was requirement of hospitalization. Data was obtained from a retrospective review of electronic health records within a New Jersey USA multi-hospital network. We compared outcomes in patients who received hydroxychloroquine with those who did not applying a multivariable logistic model with propensity matching. RESULTS: Among 1274 outpatients with documented SARS-CoV-2 infection 7.6% were prescribed hydroxychloroquine. In a 1067 patient propensity matched cohort, 21.6% with outpatient exposure to hydroxychloroquine were hospitalized, and 31.4% without exposure were hospitalized. In the primary multivariable logistic regression analysis with propensity matching there was an association between exposure to hydroxychloroquine and a decreased rate of hospitalization from COVID-19 (OR 0.53; 95% CI, 0.29, 0.95). Sensitivity analyses revealed similar associations. QTc prolongation events occurred in 2% of patients prescribed hydroxychloroquine with no reported arrhythmia events among those with data available. CONCLUSIONS: In this retrospective observational study of SARS-CoV-2 infected non-hospitalized patients hydroxychloroquine exposure was associated with a decreased rate of subsequent hospitalization. Additional exploration of hydroxychloroquine in this mildly symptomatic outpatient population is warranted.

On detecting dynamical regime change using a transformation cost metric between persistent homology diagrams.

This work outlines a pipeline for time series analysis that incorporates a measure of similarity not previously applied between homological summaries. Specifically, the well-established, but disparate, methods of persistent homology and TrAnsformation Cost Time Series (TACTS) are combined to provide a metric for tracking dynamics via changing homological features. TACTS allows subtle changes in dynamics to be accounted for, gives a quantitative output that can be directly interpreted, and is tunable to provide several complementary perspectives simultaneously. Our method is demonstrated first with known dynamical systems and then with a real-world electrocardiogram dataset. This paper highlights inadequacies in existing persistent homology metrics and describes circumstances where TACTS can be more sensitive and better suited to detecting a variety of regime changes.

Short-Term Outcomes After Multisystem Inflammatory Syndrome in Children Treatment.

This is a retrospective chart review of 20 patients treated with a consensus-driven treatment algorithm in multisystem inflammatory syndrome in children patients across a wide clinical spectrum. Their treatments and clinical status are described as well as their favorable return to functional baseline by 30 days post presentation.

Dead cells release a 'necrosignal' that activates antibiotic survival pathways in bacterial swarms.

Swarming is a form of collective bacterial motion enabled by flagella on the surface of semi-solid media. Swarming populations exhibit non-genetic or adaptive resistance to antibiotics, despite sustaining considerable cell death. Here, we show that antibiotic-induced death of a sub-population benefits the swarm by enhancing adaptive resistance in the surviving cells. Killed cells release a resistance-enhancing factor that we identify as AcrA, a periplasmic component of RND efflux pumps. The released AcrA interacts on the surface of live cells with an outer membrane component of the efflux pump, TolC, stimulating drug efflux and inducing expression of other efflux pumps. This phenomenon, which we call 'necrosignaling', exists in other Gram-negative and Gram-positive bacteria and displays species-specificity. Given that adaptive resistance is a known incubator for evolving genetic resistance, our findings might be clinically relevant to the rise of multidrug resistance.

Deep sequencing reveals new roles for MuB in transposition immunity and target-capture, and redefines the insular Ter region of E. coli.

BACKGROUND: The target capture protein MuB is responsible for the high efficiency of phage Mu transposition within the E. coli genome. However, some targets are off-limits, such as regions immediately outside the Mu ends (cis-immunity) as well as the entire ~ 37 kb genome of Mu (Mu genome immunity). Paradoxically, MuB is responsible for cis-immunity and is also implicated in Mu genome immunity, but via different mechanisms. This study was undertaken to dissect the role of MuB in target choice in vivo. RESULTS: We tracked Mu transposition from six different starting locations on the E. coli genome, in the presence and absence of MuB. The data reveal that Mu's ability to sample the entire genome during a single hop in a clonal population is independent of MuB, and that MuB is responsible for cis-immunity, plays a minor role in Mu genome immunity, and facilitates insertions into transcriptionally active regions. Unexpectedly, transposition patterns in the absence of MuB have helped extend the boundaries of the insular Ter segment of the E. coli genome. CONCLUSIONS: The results in this study demonstrate unambiguously the operation of two distinct mechanisms of Mu target immunity, only one of which is wholly dependent on MuB. The study also reveals several interesting and hitherto unknown aspects of Mu target choice in vivo, particularly the role of MuB in facilitating the capture of promoter and translation start site targets, likely by displacing macromolecular complexes engaged in gene expression. So also, MuB facilitates transposition into the restricted Ter region of the genome.

COVID-19: the effects on the practice of pediatric emergency medicine.

An informal survey of 65 pediatric emergency department leaders in North America-from 30 U.S. states and 4 Canadian provinces-revealed changes in operations, infrastructure, staffing, and clinical care that were undertaken as a result of the COVID-19 pandemic. The changes identified by the survey respondents were driven by reductions in pediatric patient volumes, a surge of adult patients, clinical considerations related to containment of infection, and financial factors. Survey respondents also reported effects of the pandemic on academic training programs and provider wellness. This report uses the survey responses to provide a snapshot of the adaptability of pediatric emergency medicine departments and clinicians during a public health emergency.

COVID-19: The impact on pediatric emergency care

Although there is still much that is not understood, experience with previous coronavirus outbreaks and available data on COVID-19 indicate a reduced propensity to affect children. Nonetheless, serious complications­although rare­are being seen in pediatric patients. This review, written with the emergency medicine clinician in mind, describes the epidemiology, clinical features, and management implications for COVID-19 in pediatric patients. It includes a discussion of multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19, as well as other aspects of the COVID-19 pandemic that are affecting children and families, such as poisonings, childhood immunizations, mental health, nonaccidental trauma, and neglect.

Mean local autocovariance provides robust and versatile choice of delay for reconstruction using frequently sampled flowlike data.

The first step in nonlinear time-series analysis can be selecting a delay for reconstruction. The most popular choices of this delay are the first zero of the autocovariance and the first minimum of the mutual information. An advantage of the first method arises from the robustness to noise of the autocovariance function, while an advantage of the second is that the first minimum of the mutual information provides a useful choice of delay for a wide range of nonlinear systems. We propose a method to choose a delay for frequently sampled flowlike data based on a mean local autocovariance function and compare its performance to methods based on the autocovariance and the mutual information. In addition, we compare the novel method to an established method based on cross-validatory mean-square errors of predictors corresponding to different choices of delay. The mean local autocovariance combines the versatility of the mutual information with some of the robustness to noise of the autocovariance.

A Well-Mixed E. coli Genome: Widespread Contacts Revealed by Tracking Mu Transposition.

The three-dimensional structures of chromosomes are increasingly being recognized as playing a major role in cellular regulatory states. The efficiency and promiscuity of phage Mu transposition was exploited to directly measure in vivo interactions between genomic loci in E. coli. Two global organizing principles have emerged: first, the chromosome is well-mixed and uncompartmentalized, with transpositions occurring freely between all measured loci; second, several gene families/regions show "clustering": strong three-dimensional co-localization regardless of linear genomic distance. The activities of the SMC/condensin protein MukB and nucleoid-compacting protein subunit HU-α are essential for the well-mixed state; HU-α is also needed for clustering of 6/7 ribosomal RNA-encoding loci. The data are explained by a model in which the chromosomal structure is driven by dynamic competition between DNA replication and chromosomal relaxation, providing a foundation for determining how region-specific properties contribute to both chromosomal structure and gene regulation.

Quadrant scan for multi-scale transition detection.

Transition detection in temporal and nontemporal signals is a problem encountered in various disciplines. We investigate the quadrant scan technique to analyze recurrence plots to identify tipping points of a dynamical system. We define two types of transition, state-transition and dynamic-transition, and prove analytically the ability of quadrant scans to detect both types. We then provide an extension by considering a weighting scheme to overcome limitations of the standard scheme. We further highlight the merits of the quadrant scan and our extension by studying several applications. The ability of the quadrant scan and its extension to deal with nontemporal, multivariate, or large data sets as well as their capability to classify multiscale transitions are demonstrated in detail through several examples and settings.

The reservoir's perspective on generalized synchronization.

We employ reservoir computing for a reconstruction task in coupled chaotic systems, across a range of dynamical relationships including generalized synchronization. For a drive-response setup, a temporal representation of the synchronized state is discussed as an alternative to the known instantaneous form. The reservoir has access to both representations through its fading memory property, each with advantages in different dynamical regimes. We also extract signatures of the maximal conditional Lyapunov exponent in the performance of variations of the reservoir topology. Moreover, the reservoir model reproduces different levels of consistency where there is no synchronization. In a bidirectional coupling setup, high reconstruction accuracy is achieved despite poor observability and independent of generalized synchronization.

On system behaviour using complex networks of a compression algorithm.

We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothesis tests. We demonstrate these ideas on systems with known dynamical behaviour and also show that our approach is capable of identifying behavioural transitions within electroencephalogram recordings as well as changes due to a bifurcation parameter of a chaotic system. The technique we propose is dependent on a coarse grained quantization of the original time series and therefore provides potential for a spatial scale-dependent characterization of the data. Finally the method is as computationally efficient as the underlying compression algorithm and provides a compression of the salient features of long time series.