Childbearing after liver transplantation

Seventeen female patients who underwent orthotopic liver transplantation between June 1973 and June 1987 became pregnant 5 months to 11 years after transplantation. Immunosuppression was maintained with combinations of prednisone, cyclosporine, and azathioprine prior to and during pregnancy. One patient discontinued immunosuppression after knowledge of pregnancy, taking only azathioprine sporadically. Mean age at time of delivery was 26 years. Twelve patients had no alteration in liver function studies; 7 patients demonstrated mild or moderate enzyme elevations prior to delivery, with one case of rejection confirmed by percutaneous liver biopsy. Major problems related to pregnancy were hypertension, anemia, and hyperbilirubinemia. Twenty live births occurred (2 patients had 2 separate pregnancies, one patient had a set of twins); 13 were by caesarian section, 7 by vaginal delivery. Eleven of the 13 caesarian births were premature by gestational age. All vaginal births were term. Toxemia of pregnancy and early rupture of membranes were the principal indications for caesarean section. There were no congenital abnormalities or birth defects and all the children are surviving well. Fifteen of 16 children older than one year all have normal physical and mental development, with one child manifesting immature speech development. Four children are under one year, all with normal milestones thus far. Sixteen of the 17 mothers are alive from 2—18 years after transplantation; the only death was from a lymphoma, almost 4 years after transplantation and 2½ years after delivery. This experience suggests that women undergoing liver transplantation can safely bear children despite an increased risk of premature caesarian births. The effect of chronic immunosuppression of female pediatric patients on their reproductive potential later in adulthood remains to be fully evaluated but the results so far are favorable. © 1990 by Williams & Wilkins

Risk measures for direct real estate investments with non-normal or unknown return distributions

The volatility of returns is probably the most widely used risk measure for real estate. This is rather surprising since a number of studies have cast doubts on the view that volatility can capture the manifold risks attached to properties and corresponds to the risk attitude of investors. A central issue in this discussion is the statistical properties of real estate returns—in contrast to neoclassical capital market theory they are mostly non-normal and often unknown, which render many statistical measures useless. Based on a literature review and an analysis of data from Germany we provide evidence that volatility alone is inappropriate for measuring the risk of direct real estate. We use a unique data sample by IPD, which includes the total returns of 939 properties across different usage types (56% office, 20% retail, 8% others and 16% residential properties) from 1996 to 2009, the German IPD Index, and the German Property Index. The analysis of the distributional characteristics shows that German real estate returns in this period were not normally distributed and that a logistic distribution would have been a better fit. This is in line with most of the current literature on this subject and leads to the question which indicators are more appropriate to measure real estate risks. We suggest that a combination of quantitative and qualitative risk measures more adequately captures real estate risks and conforms better with investor attitudes to risk. Furthermore, we present criteria for the purpose of risk classification

2021 BEETL competition: advancing transfer learning for subject independence & heterogenous EEG data sets

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because regular machine learning methods cannot generalise well across human subjects and handle learning from different, heterogeneously collected data sets, thus limiting the scale of training data available. On the other hand, the many developments in transfer- and meta-learning fields would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for all the things that make biosignal data analysis a hard problem. We design two transfer learning challenges around a. clinical diagnostics and b. neurotechnology. These two challenges are designed to probe algorithmic performance with all the challenges of biosignal data, such as low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The successful 2021 BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmarks

Microbial ligand costimulation drives neutrophilic steroid-refractory asthma

Funding: The authors thank the Wellcome Trust (102705) and the Universities of Aberdeen and Cape Town for funding. This research was also supported, in part, by National Institutes of Health GM53522 and GM083016 to DLW. KF and BNL are funded by the Fonds Wetenschappelijk Onderzoek, BNL is the recipient of an European Research Commission consolidator grant and participates in the European Union FP7 programs EUBIOPRED and MedALL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Effects of external nutrient sources and extreme weather events on the nutrient budget of a Southern European coastal lagoon

The seasonal and annual nitrogen (N), phosphorus (P), and carbon (C) budgets of the mesotidal Ria Formosa lagoon, southern Portugal, were estimated to reveal the main inputs and outputs, the seasonal patterns, and how they may influence the ecological functioning of the system. The effects of extreme weather events such as long-lasting strong winds causing upwelling and strong rainfall were assessed. External nutrient inputs were quantified; ocean exchange was assessed in 24-h sampling campaigns, and final calculations were made using a hydrodynamic model of the lagoon. Rain and stream inputs were the main freshwater sources to the lagoon. However, wastewater treatment plant and groundwater discharges dominated nutrient input, together accounting for 98, 96, and 88 % of total C, N, and P input, respectively. Organic matter and nutrients were continuously exported to the ocean. This pattern was reversed following extreme events, such as strong winds in early summer that caused upwelling and after a period of heavy rainfall in late autumn. A principal component analysis (PCA) revealed that ammonium and organic N and C exchange were positively associated with temperature as opposed to pH and nitrate. These variables reflected mostly the benthic lagoon metabolism, whereas particulate P exchange was correlated to Chl a, indicating that this was more related to phytoplankton dynamics. The increase of stochastic events, as expected in climate change scenarios, may have strong effects on the ecological functioning of coastal lagoons, altering the C and nutrient budgets.Portuguese Science and Technology Foundation (FCT) [POCI/MAR/58427/2004, PPCDT/MAR/58427/2004]; Portuguese Science and Technology Foundation (FCT

Galactic and Extragalactic Samples of Supernova Remnants: How They Are Identified and What They Tell Us

Supernova remnants (SNRs) arise from the interaction between the ejecta of a supernova (SN) explosion and the surrounding circumstellar and interstellar medium. Some SNRs, mostly nearby SNRs, can be studied in great detail. However, to understand SNRs as a whole, large samples of SNRs must be assembled and studied. Here, we describe the radio, optical, and X-ray techniques which have been used to identify and characterize almost 300 Galactic SNRs and more than 1200 extragalactic SNRs. We then discuss which types of SNRs are being found and which are not. We examine the degree to which the luminosity functions, surface-brightness distributions and multi-wavelength comparisons of the samples can be interpreted to determine the class properties of SNRs and describe efforts to establish the type of SN explosion associated with a SNR. We conclude that in order to better understand the class properties of SNRs, it is more important to study (and obtain additional data on) the SNRs in galaxies with extant samples at multiple wavelength bands than it is to obtain samples of SNRs in other galaxiesComment: Final 2016 draft of a chapter in "Handbook of Supernovae" edited by Athem W. Alsabti and Paul Murdin. Final version available at https://doi.org/10.1007/978-3-319-20794-0_90-

Dual Energy X-Ray Absorptiometry Body Composition Reference Values from NHANES

In 2008 the National Center for Health Statistics released a dual energy x-ray absorptiometry (DXA) whole body dataset from the NHANES population-based sample acquired with modern fan beam scanners in 15 counties across the United States from 1999 through 2004. The NHANES dataset was partitioned by gender and ethnicity and DXA whole body measures of %fat, fat mass/height2, lean mass/height2, appendicular lean mass/height2, %fat trunk/%fat legs ratio, trunk/limb fat mass ratio of fat, bone mineral content (BMC) and bone mineral density (BMD) were analyzed to provide reference values for subjects 8 to 85 years old. DXA reference values for adults were normalized to age; reference values for children included total and sub-total whole body results and were normalized to age, height, or lean mass. We developed an obesity classification scheme by using estabbody mass index (BMI) classification thresholds and prevalences in young adults to generate matching classification thresholds for Fat Mass Index (FMI; fat mass/height2). These reference values should be helpful in the evaluation of a variety of adult and childhood abnormalities involving fat, lean, and bone, for establishing entry criteria into clinical trials, and for other medical, research, and epidemiological uses

Heterosynaptic plasticity in the neocortex

Ongoing learning continuously shapes the distribution of neurons’ synaptic weights in a system with plastic synapses. Plasticity may change the weights of synapses that were active during the induction—homosynaptic changes, but also may change synapses not active during the induction—heterosynaptic changes. Here we will argue, that heterosynaptic and homosynaptic plasticity are complementary processes, and that heterosynaptic plasticity might accompany homosynaptic plasticity induced by typical pairing protocols. Synapses are not uniform in their susceptibility for plastic changes, but have predispositions to undergo potentiation or depression, or not to change. Predisposition is one of the factors determining the direction and magnitude of homo- and heterosynaptic changes. Heterosynaptic changes which take place according to predispositions for plasticity may provide a useful mechanism(s) for homeostasis of neurons’ synaptic weights and extending the lifetime of memory traces during ongoing learning in neuronal networks