Causal connectivity measures for pulse-output network reconstruction: Analysis and applications.

The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four commonly utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking a simulated Hodgkin-Huxley network and a real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore illustrating a direct link between the causal and structural connectivity. We stress that the structural connectivity of pulse-output networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.

A biochemical description of postsynaptic plasticity-with timescales ranging from milliseconds to seconds.

Synaptic plasticity [long-term potentiation/depression (LTP/D)], is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally-spike timing dependent plasticity (STDP), on time scales of tens of ms; and behavioral time scale synaptic plasticity (BTSP), on time scales of seconds. BTSP is a candidate for a mechanism underlying rapid learning of spatial location by place cells. Here, a computational model of the induction of E-LTP/D at a spine head of a synapse of a hippocampal pyramidal neuron is developed. The single-compartment model represents two interacting biochemical pathways for the activation (phosphorylation) of the kinase (CaMKII) with a phosphatase, with ion inflow through channels (NMDAR, CaV1,Na). The biochemical reactions are represented by a deterministic system of differential equations, with a detailed description of the activation of CaMKII that includes the opening of the compact state of CaMKII. This single model captures realistic responses (temporal profiles with the differing timescales) of STDP and BTSP and their asymmetries. The simulations distinguish several mechanisms underlying STDP vs. BTSP, including i) the flow of [Formula: see text] through NMDAR vs. CaV1 channels, and ii) the origin of several time scales in the activation of CaMKII. The model also realizes a priming mechanism for E-LTP that is induced by [Formula: see text] flow through CaV1.3 channels. Once in the spine head, this small additional [Formula: see text] opens the compact state of CaMKII, placing CaMKII ready for subsequent induction of LTP.

Modeling the role of gap junctions between excitatory neurons in the developing visual cortex.

Recent experiments in the developing mammalian visual cortex have revealed that gap junctions couple excitatory cells and potentially influence the formation of chemical synapses. In particular, cells that were coupled by a gap junction during development tend to share an orientation preference and are preferentially coupled by a chemical synapse in the adult cortex, a property that is diminished when gap junctions are blocked. In this work, we construct a simplified model of the developing mouse visual cortex including spike-timing-dependent plasticity of both the feedforward synaptic inputs and recurrent cortical synapses. We use this model to show that synchrony among gap-junction-coupled cells underlies their preference to form strong recurrent synapses and develop similar orientation preference; this effect decreases with an increase in coupling density. Additionally, we demonstrate that gap-junction coupling works, together with the relative timing of synaptic development of the feedforward and recurrent synapses, to determine the resulting cortical map of orientation preference.

Reclassification of Pterulaceae Corner (Basidiomycota: Agaricales) introducing the ant-associated genus Myrmecopterula gen. nov., Phaeopterula Henn. and the corticioid Radulomycetaceae fam. nov.

Pterulaceae was formally proposed to group six coralloid and dimitic genera: Actiniceps (=Dimorphocystis), Allantula, Deflexula, Parapterulicium, Pterula, and Pterulicium. Recent molecular studies have shown that some of the characters currently used in Pterulaceae do not distinguish the genera. Actiniceps and Parapterulicium have been removed, and a few other resupinate genera were added to the family. However, none of these studies intended to investigate the relationship between Pterulaceae genera. In this study, we generated 278 sequences from both newly collected and fungarium samples. Phylogenetic analyses supported with morphological data allowed a reclassification of Pterulaceae where we propose the introduction of Myrmecopterula gen. nov. and Radulomycetaceae fam. nov., the reintroduction of Phaeopterula, the synonymisation of Deflexula in Pterulicium, and 53 new combinations. Pterula is rendered polyphyletic requiring a reclassification; thus, it is split into Pterula, Myrmecopterula gen. nov., Pterulicium and Phaeopterula. Deflexula is recovered as paraphyletic alongside several Pterula species and Pterulicium, and is sunk into the latter genus. Phaeopterula is reintroduced to accommodate species with darker basidiomes. The neotropical Myrmecopterula gen. nov. forms a distinct clade adjacent to Pterula, and most members of this clade are associated with active or inactive attine ant nests. The resupinate genera Coronicium and Merulicium are recovered in a strongly supported clade close to Pterulicium. The other resupinate genera previously included in Pterulaceae, and which form basidiomes lacking cystidia and with monomitic hyphal structure (Radulomyces, Radulotubus and Aphanobasidium), are reclassified into Radulomycetaceae fam. nov. Allantula is still an enigmatic piece in this puzzle known only from the type specimen that requires molecular investigation. A key for the genera of Pterulaceae and Radulomycetaceae fam. nov. is also provided here.

Neural networks of different species, brain areas and states can be characterized by the probability polling state.

Cortical networks are complex systems of a great many interconnected neurons that operate from collective dynamical states. To understand how cortical neural networks function, it is important to identify their common dynamical operating states from the probabilistic viewpoint. Probabilistic characteristics of these operating states often underlie network functions. Here, using multi-electrode data from three separate experiments, we identify and characterize a cortical operating state (the "probability polling" or "p-polling" state), common across mouse and monkey with different behaviors. If the interaction among neurons is weak, the p-polling state provides a quantitative understanding of how the high dimensional probability distribution of firing patterns can be obtained by the low-order maximum entropy formulation, effectively utilizing a low dimensional stimulus-coding structure. These results show evidence for generality of the p-polling state and in certain situations its advantage of providing a mathematical validation for the low-order maximum entropy principle as a coding strategy.

Ring models of binocular rivalry and fusion.

When similar visual stimuli are presented binocularly to both eyes, one perceives a fused single image. However, when the two stimuli are distinct, one does not perceive a single image; instead, one perceives binocular rivalry. That is, one perceives one of the stimulated patterns for a few seconds, then the other for few seconds, and so on - with random transitions between the two percepts. Most theoretical studies focus on rivalry, with few considering the coexistence of fusion and rivalry. Here we develop three distinct computational neuronal network models which capture binocular rivalry with realistic stochastic properties, fusion, and the hysteretic transition between. Each is a conductance-based point neuron model, which is multi-layer with two ocular dominance columns (L & R) and with an idealized "ring" architecture where the orientation preference of each neuron labels its location on a ring. In each model, the primary mechanism initiating binocular rivalry is cross-column inhibition, with firing rate adaptation governing the temporal properties of the transitions between percepts. Under stimulation by similar visual patterns, each of three models uses its own mechanism to overcome cross-column inhibition, and thus to prevent rivalry and allow the fusion of similar images: The first model uses cross-column feedforward inhibition from the opposite eye to "shut off" the cross-column feedback inhibition; the second model "turns on" a second layer of monocular neurons as a parallel pathway to the binocular neurons, rivaling out of phase with the first layer, and together these two pathways represent fusion; and the third model uses cross-column excitation to overcome the cross-column inhibition and enable fusion. Thus, each of the idealized ring models depends upon a different mechanism for fusion that might emerge as an underlying mechanism present in real visual cortex.

Occupational Contamination with a Highly Enriched Uranium Solution.

This medical case report describes the first reported instance of occupational skin contamination with a uranyl nitrate solution containing highly enriched uranium. The report provides an overview of the unique medical treatment and management considerations in such a case. Internal dose assessment is covered in detail. The discussion covers key points regarding uranium characteristics, chemical and radiological damage to body tissues from HEU exposure, and resources available for assistance with a case of radiological contamination. This information adds to the limited medical literature on this topic and provides a valuable reference for medical personnel when dealing with this uncommon problem.

Dendritic computations captured by an effective point neuron model.

Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework.

AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.

Contouring (segmentation) of Organs at Risk (OARs) in medical images is required for accurate radiation therapy (RT) planning. In current clinical practice, OAR contouring is performed with low levels of automation. Although several approaches have been proposed in the literature for improving automation, it is difficult to gain an understanding of how well these methods would perform in a realistic clinical setting. This is chiefly due to three key factors - small number of patient studies used for evaluation, lack of performance evaluation as a function of input image quality, and lack of precise anatomic definitions of OARs. In this paper, extending our previous body-wide Automatic Anatomy Recognition (AAR) framework to RT planning of OARs in the head and neck (H&N) and thoracic body regions, we present a methodology called AAR-RT to overcome some of these hurdles. AAR-RT follows AAR's 3-stage paradigm of model-building, object-recognition, and object-delineation. Model-building: Three key advances were made over AAR. (i) AAR-RT (like AAR) starts off with a computationally precise definition of the two body regions and all of their OARs. Ground truth delineations of OARs are then generated following these definitions strictly. We retrospectively gathered patient data sets and the associated contour data sets that have been created previously in routine clinical RT planning from our Radiation Oncology department and mended the contours to conform to these definitions. We then derived an Object Quality Score (OQS) for each OAR sample and an Image Quality Score (IQS) for each study, both on a 1-to-10 scale, based on quality grades assigned to each OAR sample following 9 key quality criteria. Only studies with high IQS and high OQS for all of their OARs were selected for model building. IQS and OQS were employed for evaluating AAR-RT's performance as a function of image/object quality. (ii) In place of the previous hand-crafted hierarchy for organizing OARs in AAR, we devised a method to find an optimal hierarchy for each body region. Optimality was based on minimizing object recognition error. (iii) In addition to the parent-to-child relationship encoded in the hierarchy in previous AAR, we developed a directed probability graph technique to further improve recognition accuracy by learning and encoding in the model "steady" relationships that may exist among OAR boundaries in the three orthogonal planes. Object-recognition: The two key improvements over the previous approach are (i) use of the optimal hierarchy for actual recognition of OARs in a given image, and (ii) refined recognition by making use of the trained probability graph. Object-delineation: We use a kNN classifier confined to the fuzzy object mask localized by the recognition step and then fit optimally the fuzzy mask to the kNN-derived voxel cluster to bring back shape constraint on the object. We evaluated AAR-RT on 205 thoracic and 298 H&N (total 503) studies, involving both planning and re-planning scans and a total of 21 organs (9 - thorax, 12 - H&N). The studies were gathered from two patient age groups for each gender - 40-59 years and 60-79 years. The number of 3D OAR samples analyzed from the two body regions was 4301. IQS and OQS tended to cluster at the two ends of the score scale. Accordingly, we considered two quality groups for each gender - good and poor. Good quality data sets typically had OQS ≥ 6 and had distortions, artifacts, pathology etc. in not more than 3 slices through the object. The number of model-worthy data sets used for training were 38 for thorax and 36 for H&N, and the remaining 479 studies were used for testing AAR-RT. Accordingly, we created 4 anatomy models, one each for: Thorax male (20 model-worthy data sets), Thorax female (18 model-worthy data sets), H&N male (20 model-worthy data sets), and H&N female (16 model-worthy data sets). On "good" cases, AAR-RT's recognition accuracy was within 2 voxels and delineation boundary distance was within ∼1 voxel. This was similar to the variability observed between two dosimetrists in manually contouring 5-6 OARs in each of 169 studies. On "poor" cases, AAR-RT's errors hovered around 5 voxels for recognition and 2 voxels for boundary distance. The performance was similar on planning and replanning cases, and there was no gender difference in performance. AAR-RT's recognition operation is much more robust than delineation. Understanding object and image quality and how they influence performance is crucial for devising effective object recognition and delineation algorithms. OQS seems to be more important than IQS in determining accuracy. Streak artifacts arising from dental implants and fillings and beam hardening from bone pose the greatest challenge to auto-contouring methods.

Revealing Active Sites for Hydrogen Evolution at Pt and Pd Atomic Layers on Au Surfaces.

Identification of the most active surface sites is one of the key tasks in the development of new electrocatalytic materials. This is in many cases both time and resource consuming due to methodological difficulties of in situ detection of centers of this kind. In this work, we use the recently developed approach based on the analysis of the tunneling current noise recorded by electrochemical scanning tunneling microscopy (n-ECSTM) to compare the nature of the most active hydrogen evolution catalytic sites in a system consisting of sub-monolayers of platinum on a Au substrate to the one of palladium on Au. Our n-ECSTM measurements performed under reaction conditions show that in striking contrast to Pd islands on gold, where the most active centers are located close to the boundary between Au and palladium atoms, all Pt ad-atoms contribute to the overall activity rather equally at pH 1. Methodological aspects related to the use of n-ECSTM in electrocatalytic research are also discussed.

Mechanisms underlying contrast-dependent orientation selectivity in mouse V1.

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties-one of which is that contrast invariance in the orientation selectivity (OS) of the neurons' firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties-for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance-providing insights for future studies on contrast dependence (invariance).

Auto-contouring via Automatic Anatomy Recognition of Organs at Risk in Head and Neck Cancer on CT images.

Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H&N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images. Our method has three major steps: model building, object recognition, and object delineation. First, the better-quality images from our cohort of H&N CT studies are used to build fuzzy models and find the optimal hierarchy for arranging objects based on the relationship between objects. Then, the object recognition step exploits the rich prior anatomic information encoded in the hierarchy to derive the location and pose for each object, which leads to generalizable and robust methods and mitigation of object localization challenges. Finally, the delineation algorithms employ local features to contour the boundary based on object recognition results. We make several improvements within the AAR framework, including finding recognition-error-driven optimal hierarchy, modeling boundary relationships, combining texture and intensity, and evaluating object quality. Experiments were conducted on the largest ensemble of clinical data sets reported to date, including 216 planning CT studies and over 2,600 object samples. The preliminary results show that on data sets with minimal (<4 slices) streak artifacts and other deviations, overall recognition accuracy reaches 2 voxels, with overall delineation Dice coefficient close to 0.8 and Hausdorff Distance within 1 voxel.

Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning.

Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous low-level process of precisely indicating the space occupied by an object. This study focuses on recognition. The purpose of this paper is to introduce new features of the AAR-recognition approach (abbreviated as AAR-R from now on) of combining texture and intensity information into the recognition procedure, using the optimal spanning tree to achieve the optimal hierarchy for recognition to minimize recognition errors, and to illustrate recognition performance by using large-scale testing computed tomography (CT) data sets. The data sets pertain to 216 non-serial (planning) and 82 serial (re-planning) studies of head and neck (H&N) cancer patients undergoing radiation therapy, involving a total of ~2600 object samples. Texture property "maximum probability of occurrence" derived from the co-occurrence matrix was determined to be the best property and is utilized in conjunction with intensity properties in AAR-R. An optimal spanning tree is found in the complete graph whose nodes are individual objects, and then the tree is used as the hierarchy in recognition. Texture information combined with intensity can significantly reduce location error for gland-related objects (parotid and submandibular glands). We also report recognition results by considering image quality, which is a novel concept. AAR-R with new features achieves a location error of less than 4 mm (~1.5 voxels in our studies) for good quality images for both serial and non-serial studies.

Image Quality and Segmentation.

Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.

Comparison of measured electron energy spectra for six matched, radiotherapy accelerators.

This study compares energy spectra of the multiple electron beams of individual radiotherapy machines, as well as the sets of spectra across multiple matched machines. Also, energy spectrum metrics are compared with central-axis percent depth-dose (PDD) metrics. METHODS: A lightweight, permanent magnet spectrometer was used to measure energy spectra for seven electron beams (7-20 MeV) on six matched Elekta Infinity accelerators with the MLCi2 treatment head. PDD measurements in the distal falloff region provided R50 and R80-20 metrics in Plastic Water® , which correlated with energy spectrum metrics, peak mean energy (PME) and full-width at half maximum (FWHM). RESULTS: Visual inspection of energy spectra and their metrics showed whether beams on single machines were properly tuned, i.e., FWHM is expected to increase and peak height decrease monotonically with increased PME. Also, PME spacings are expected to be approximately equal for 7-13 MeV beams (0.5-cm R90 spacing) and for 13-16 MeV beams (1.0-cm R90 spacing). Most machines failed these expectations, presumably due to tolerances for initial beam matching (0.05 cm in R90 ; 0.10 cm in R80-20 ) and ongoing quality assurance (0.2 cm in R50 ). Also, comparison of energy spectra or metrics for a single beam energy (six machines) showed outlying spectra. These variations in energy spectra provided ample data spread for correlating PME and FWHM with PDD metrics. Least-squares fits showed that R50 and R80-20 varied linearly and supralinearly with PME, respectively; however, both suggested a secondary dependence on FWHM. Hence, PME and FWHM could serve as surrogates for R50 and R80-20 for beam tuning by the accelerator engineer, possibly being more sensitive (e.g., 0.1 cm in R80-20 corresponded to 2.0 MeV in FWHM). CONCLUSIONS: Results of this study suggest a lightweight, permanent magnet spectrometer could be a useful beam-tuning instrument for the accelerator engineer to (a) match electron beams prior to beam commissioning, (b) tune electron beams for the duration of their clinical use, and (c) provide estimates of PDD metrics following machine maintenance. However, a real-time version of the spectrometer is needed to be practical.

Structural character evolution in Pucciniomycotina: mitosis, septa, and hyphal branch initiation in two Helicogloea species.

Early diverging taxa of Ascomycota and Basidiomycota share similarities in subcellular characters of the spindle pole body (SPB), nuclear division, and septal pore apparatus, but our understanding of character evolution is incomplete because of the limited number of structural studies within the earliest diverging subphyla of Dikarya, Taphrinomycotina and Pucciniomycotina. Two species of Helicogloea (Atractiellomycetes) were analyzed for these characters and provide data on SPB and nuclear division for an additional class of Pucciniomycotina. A detailed analysis of septal pore apparatus for the Helicogloea species permits comparisons with those of other Pucciniomycotina and Ascomycota. The endogenous origin of hyphal branches is shown to occur in a third class of Pucciniomycotina. The full set of characters supports a close relationship between Atractiellomycetes and Pucciniomycetes.

A molecular and morphological re-examination of the generic limits of truffles in the tarzetta-geopyxis lineage - Densocarpa, Hydnocystis, and Paurocotylis.

Truffle species within the /tarzetta-geopyxis lineage share smooth, globose, hyaline spores, but differ in the amount of convolution of hymenia in ascomata. The relationships among truffle species in this lineage have historically been confused. Phylogenetic analyses of the ITS and 28S nuclear ribosomal DNA from recently collected members of the /tarzetta-geopyxis lineage from Asia, Austral Asia, North America, and South America prompted a reinvestigation of species and generic limits in the truffle genera Hydnocystis, Paurocotylis, and Stephensia. Our analyses support emendations of Hydnocystis and Paurocotylis, abandonment of Stephensia and the resurrection of the genus Densocarpa. Nomenclatural changes include the transfer of Stephensia bombycina to Hydnocystis, the transfer of Hydnocystis singeri and Stephensia bynumii to Paurocotylis, the reinstatement of Densocarpa for Stephensia shanori and transfer of Stephensia crocea to Densocarpa. This is the first detection of the genus Paurocotylis in the Americas. We describe three new species, Hydnocystis transitoria from North America, Paurocotylis patagonica from South America, and Paurocotylis watlingii from Australia. Our work highlights the unexplored diversity, morphological plasticity, and remaining taxonomic problems among truffles in the /tarzetta-geopyxis lineage.

Management of Type 2B von Willebrand Disease during Pregnancy.

Type 2B von Willebrand disease is a rare bleeding condition resulting in thrombocytopenia and a reduction in large VWF multimers. It usually has an autosomal dominant pattern of inheritance. We report the management of a patient with type 2B von Willebrand disease, whose diagnosis was confirmed by demonstration of a R1306W mutation, through her first pregnancy. The patient's von Willebrand factor (VWF) antigen and VWF ristocetin cofactor levels rose throughout pregnancy, with an associated drop in the platelet count. The patient was successfully managed through labour to a surgical delivery with VWF concentrate, platelet transfusions and tranexamic acid. The patient delivered a male baby who was found to have inherited type 2B von Willebrand disease and had a significant cephalhaematoma at delivery. The baby was managed with VWF concentrate and platelet transfusions and made a full recovery. There is a lack of evidence to guide the best management of pregnant patients with type 2B von Willebrand disease. We adopted a pragmatic management plan, in keeping with other published case reports. To the best of our knowledge, this is the first case report in which the child was found to have inherited type 2B von Willebrand disease and encountered bleeding problems, making this case unique amongst the published literature.

Cultural and cytological characterization of Dacryopinax primogenitus, a new species in the Dacrymycetes with a fully sequenced genome.

A Dacryopinax species that was cultured in Costa Rica and fruited in the laboratory provided DNA for the first sequenced genome for the Dacrymycetes. Here we characterize the isolate morphologically and cytologically and name it D. primogenitus Molecular sequences from the nuclear large subunit gene and internal transcribed spacer indicated that it is closely related to the South American D. indacocheae with which it agrees structurally. Both species form conidia on the basidiocarp, and D primogenitus also forms them on the mycelium. Unlike previous reports for the Dacrymycetales postmeiotic nuclear division results in uninucleate basidiospores and six residual nuclei in the basidium after basidiospore discharge. Ultrastructural analysis shows the characteristic septal-pore apparatus for the class and endogenous origin of the epibasidia/sterigmata, which may be a common occurrence in Dacrymycetes and the early diverging orders of its sister class, the Agaricomycetes.