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The vegetation of the miombo woodland is a highly heterogeneous phenomenon which makes it hard to assess biomass. Hence very little is known of their carbon dynamics and factors causing biomass variations. Estimating forest biomass is the first step towards carbon stock calculation. Current knowledge of miombo’s carbon (C) pools is limited despite its importance in the global C budget. The article will address questions on how soil and vegetation carbon stocks differ across a miombo woodland landscape to enhance understanding of C stocks in African woodlands, and to what degree and scale are those stocks linked? A 5 km transect cyclic sampling scheme was used to allow geostatistical analysis. Vegetation and soil C stocks were coupled in the landscape in the top 5 cm of soil (r2 = 0.24) but not with deeper soil C stocks, which were coupled to soil clay content (r2 = 0.38). This study suggests that C stock distributions are strongly linked to topography and soil texture. To optimise sampling strategies for C stock assessments in miombo, soil C should be sampled at > 26 m apart, and AG C should be sampled at > 1430 m apart in plots > 0.5 ha.

Summary In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

MotivationThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the relationships among the microbes of a microbiota. In this high-dimensional setting, the zero-inflated and compositional data structure (subject to unit-sum constraint) pose challenges to the accurate estimation of microbial interaction networks.MethodWe propose thezero-inflated latent Ising(ZILI) model for microbial interaction network which assumes that the distribution of relative abundance of microbiota is determined by finite latent states. This assumption is partly supported by the existing findings in literature [20]. The ZILI model can circumvents the unit-sum constraint and alleviates the zero-inflation problem under given assumptions. As for the model selection of ZILI, a two-step algorithm is proposed. ZILI and two-step algorithm are evaluated through simulated data and subsequently applied in our investigation of an infant gut microbiome dataset from New Hampshire Birth Cohort Study. The results are compared with results from traditional Gaussian graphical model (GGM) and dichotomous Ising model (DIS).ResultsThrough the simulation studies, provided that the ZILI model is the true generative model for the data, it is shown that the two-step algorithm can estimate the graphical structure effectively and is robust to a range of alternative settings of the related factors. Both GGM and DIS can not achieve a satisfying performance in these settings. For the infant gut microbiome dataset, we use both ZILI and GGM to estimate microbial interaction network. The final estimated networks turn out to share a statistically significant overlap in which the ZILI and two-step algorithm tend to select the sparser network than those modeled by GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.AvailabilityThe data and programs involved in Section 4 and 5 are available on request from the correspondence author.ContactAnne.G.Hoen@dartmouth.eduSupplementary informationSupplementary materials are available atBioinformatics

Background\ud \ud Emergency school closures are often used as public health interventions during infectious disease outbreaks to minimise the spread of infection. However, if children continue mixing with others outside the home during closures, the effect of these measures may be limited.\ud \ud \ud \ud Aim\ud \ud This review aimed to summarise existing literature on children’s activities and contacts made outside the home during unplanned school closures.\ud \ud \ud \ud Methods\ud \ud In February 2020, we searched four databases, MEDLINE, PsycInfo, Embase and Web of Science, from inception to 5 February 2020 for papers published in English or Italian in peer-reviewed journals reporting on primary research exploring children’s social activities during unplanned school closures. Main findings were extracted.\ud \ud \ud \ud Results\ud \ud A total of 3,343 citations were screened and 19 included in the review. Activities and social contacts appeared to decrease during closures, but contact remained common. All studies reported children leaving the home or being cared for by non-household members. There was some evidence that older child age (two studies) and parental disagreement (two studies) with closure were predictive of children leaving the home, and mixed evidence regarding the relationship between infection status and such. Parental agreement with closure was generally high, but some disagreed because of perceived low risk of infection and issues regarding childcare and financial impact.\ud \ud \ud \ud Conclusion\ud \ud Evidence suggests that many children continue to leave home and mix with others during school closures despite public health recommendations to avoid social contact. This review of behaviour during unplanned school closures could be used to improve infectious disease modelling.

The inductor presents one of the biggest obstacles in miniaturizing power electronics. Inductive circuit elements are required by almost all switch mode converter topologies but inductors are relatively large and lossy. Piezoelectric resonators can provide an inductive impedance around resonance with low loss, offering the possibility of replacing inductors and increasing power density. High frequencies are needed to achieve high power density with piezoelectric resonators, which is now achievable with wide band-gap semiconductors. In this paper we present a piezoelectric resonator for power conversion fabricated out of lithium niobate, a material that has several advantages for high frequency power conversion compared to the more commonly used PZT ceramics. The piezoelectric resonator is used in a step-down dc-dc converter that achieves zero voltage soft switching and a continuously variable conversion ratio without the use of magnetic components. The converter operates at 6.3MHz and has a maximum power output of 30W at an efficiency of 92.7%, achieving a power density of 16.7 W/cm3

Stereo digital image correlation (DIC) is a non-contacting technique to obtain full-field displacement and strain fields from the surface of a deforming object by comparing images recorded before and after deformation. With stereo measurement techniques, out-of-plane measurements are often limited by the depth of field achieved in an image. Tilt-shift photography uses the Scheimpflug principle to improve depth of field by rotating the image plane with respect to the camera sensor. This is implemented using a tilt-shift lens mount, which imposes an angle of tilt between the lens and the camera sensor. In this stereo-DIC study, images were taken using a range of stereo camera angles with either the lens tilted or not tilted to impose the Scheimpflug effect. A series of rigid body displacements were then measured for each combination of stereo and tilt angles. An evaluation of the means and standard deviations of the measurements shows that Scheimpflug adjustment offers minimal improvement when measuring rigid body displacements, but significant improvement at reducing artificial strains.

Additional file 1 The file Networks.pdf includes the gut microbial interaction networks selected by ZILI model and Gaussian graphical model with thresholds, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% respectively. These networks are used in Table 2 to investigate the relationship between ZILI and GGM.

Archaeological Investigations Ltd unpublished report series, 0

As new applications are emerging for very-high-frequency power converters, the demand for magnetic components suitable for these frequency regimes increase. Currently, limited magnetic core materials are available above 10 MHz, therefore making air-core components the primary option. This paper presents a nested toroidal air-core transformer utilizing a 3-D printing and electroplating fabrication methodology. The first part of this work discusses design equations to achieve the target inductance matrix. Then, we provide finite-element-method (FEM) simulations in COMSOL with automation and scripting capabilities, allowing circuit designers straightforward methods in obtaining transformer parameters based on known dimensions. Lastly, this work provides experimental demonstrations of the transformer design including a 40.68 MHz 48-to-350 V resonant dc-dc converter.

Typical MRI power amplifiers are characterized by the requirement for high linearity and a high peak-to-average power ratio. When implemented as a linear amplifier, such as the commonly used Class-AB architecture this leads to a low efficiency and high heat dissipation, which impacts performance and reliability. Here we present a MRI compatible DC modulator for envelope tracking applications based on a 4-phase interleaved buck converter using air core magnetics and utilizing a FPGA based digital controller. The modulator is capable of delivering 500 W into a 2.5 Ω load at an efficiency of 95.6%, has a 3 dB modulation bandwidth of 50 kHz, and has been tested with a Class-AB linear amplifier operating at 64 MHz to produce a modulated RF envelope with a peak power of 320 W. Due to the higher efficiency of envelope tracking amplifiers and the ability to mount the amplifier directly on the MRI machine itself this system has the potential to decrease both the cost and space requirements of MRI machines.

SummaryObjectives Interictal epileptiform discharges (IEDs) have been linked to memory impairment, but the spatial and temporal dynamics of this relationship remain elusive. In the present study, we aim to systematically characterize the brain areas and times at which IEDs affect memory. Methods Eighty epilepsy patients participated in a delayed free recall task while undergoing intracranial electroencephalography (EEG) monitoring. We analyzed the locations and timing of IEDs relative to the behavioral data in order to measure their effects on memory. Results Overall IED rates did not correlate with task performance across subjects (r = 0.03, p = 0.8). However, at a finer temporal scale, within-subject memory was negatively affected by IEDs during the encoding and recall periods of the task but not during the rest and distractor periods (p < 0.01, p < 0.001, p = 0.3, and p = 0.8, respectively). The effects of IEDs during encoding and recall were stronger in the left hemisphere than in the right (p < 0.05). Of six brain areas analyzed, IEDs in the inferior-temporal, medial-temporal, and parietal areas significantly affected memory (false discovery rate < 0.05). Significance These findings reveal a network of brain areas sensitive to IEDs with key nodes in temporal as well as parietal lobes. They also demonstrate the time-dependent effects of IEDs in this network on memory.