An Evaluation of the Relationship Between Medication Regimen Complexity as Measured by the MRC-ICU to Medication Errors in Critically Ill Patients.

Purpose: The purpose of this study was to determine the relationship between medication regimen complexity-intensive care unit (MRC-ICU) score at 24 hours and medication errors identified throughout the ICU. Methods: A single-center, observational study was conducted from August to October 2021. The primary outcome was the association between MRC-ICU at 24 hours and total medication errors identified. During the prospective component, ICU pharmacists recorded medication errors identified over an 8-week period. During the retrospective component, the electronic medical record was reviewed to collect patient demographics, outcomes, and MRC-ICU score at 24 hours. The primary outcome of the relationship of MRC-ICU at 24 hours to medication errors was assessed using Pearson correlation. Results: A total of 150 patients were included. There were 2 pharmacists who recorded 634 errors during the 8-week study period. No significant relationship between MRC-ICU and medication errors was observed (r2 = .13, P = .11). Exploratory analyses of MRC-ICU relationship to major interventions and harm scores showed that MRC-ICU scores >10 had more major interventions (27 vs 14, P = .27) and higher harm scores (15 vs 7, P = .33), although these values were not statistically significant. Conclusion: Medication errors appear to occur independently of medication regimen complexity. Critical care pharmacists were responsible for mitigating a large number of medication errors.

Functional Output Regression for Machine Learning in Materials Science.

In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we consider two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in material science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modeling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it effective in modeling even with small amounts of data. We demonstrate the proposed methods through several case studies in materials science.

Porcine reproductive and respiratory syndrome virus genetic variability a management and diagnostic dilemma.

As genetic analysis becomes less expensive, more comprehensive diagnostics such as whole genome sequencing (WGS) will become available to the veterinary practitioner. The WGS elucidates more about porcine reproductive and respiratory syndrome virus (PRRSV) beyond the traditional analysis of open reading frame (ORF) 5 Sanger sequencing. The veterinary practitioner will require a more complete understanding of the mechanics and consequences of PRRSV genetic variability to interpret the WGS results. More recently, PRRSV recombination events have been described in the literature. The objective of this review is to provide a comprehensive outlook for swine practitioners that PRRSV mutates and recombines naturally causing genetic variability, review the diagnostic cadence when suspecting recombination has occurred, and present theory on how, why, and where industry accepted management practices may influence recombination. As practitioners, it is imperative to remember that PRRS viral recombination is occurring continuously in swine populations. Finding a recombinant by diagnostic analysis does not ultimately declare its significance. The error prone replication, mutation, and recombination of PRRSV means exact clones may exist; but a quasispecies swarm of variable strains also exist adding to the genetic diversity. PRRSV nonstructural proteins (nsps) are translated from ORF1a and ORF1b. The arterivirus nsps modulate the hosts' immune response and are involved in viral pathogenesis. The strains that contribute the PRRSV replicase and transcription complex is driving replication and possibly recombination in the quasispecies swarm. Furthermore, mutations favoring the virus to evade the immune system may result in the emergence of a more fit virus. More fit viruses tend to become the dominant strains in the quasispecies swarm. In theory, the swine management practices that may exacerbate or mitigate recombination include immunization strategies, swine movements, regional swine density, and topography. Controlling PRRSV equates to managing the quasispecies swarm and its interaction with the host. Further research is warranted on the frequency of recombination and the genome characteristics impacting the recombination rate. With a well-defined understanding of these characteristics, the clinical implications from recombination can be detected and potentially reduced; thus, minimizing recombination and perhaps the emergence of epidemic strains.

Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans.

BACKGROUND: Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons. RESULTS: Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging. CONCLUSION: Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.

RNA matchmaking in chromatin regulation.

Beyond being the product of gene expression, RNA can also influence the regulation of chromatin. The majority of the human genome has the capacity to be transcribed and the majority of the non-protein-coding transcripts made by RNA Polymerase II are enriched in the nucleus. Many chromatin regulators can bind to these ncRNAs in the nucleus; in some cases, there are clear examples of direct RNA-mediated chromatin regulation mechanisms stemming from these interactions, while others have yet to be determined. Recent studies have highlighted examples of chromatin regulation via RNA matchmaking, a term we use broadly here to describe intermolecular base-pairing interactions between one RNA molecule and an RNA or DNA match. This review provides examples of RNA matchmaking that regulates chromatin processes and summarizes the technical approaches used to capture these events.

Bayesian Algorithm for Retrosynthesis.

The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion of the entire reaction space. At present, emerging machine learning technologies are reformulating the process of retrosynthetic planning. This study aimed to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of the training of a deep neural network, which is used to forwardly predict a product of the given reactants with a high level of accuracy, followed by inversion of the forward model into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. With a forward model prediction accuracy of approximately 87%, the Bayesian retrosynthesis algorithm successfully rediscovered 81.8 and 33.3% of known synthetic routes of one-step and two-step reactions, respectively, with top-10 accuracy. Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We also investigated the potential applicability of such diverse candidates based on expert knowledge of synthetic organic chemistry.

A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes.

OBJECTIVE: This study aims to develop and evaluate effective methods that can normalize diagnosis and procedure terms written by physicians to standard concepts in International Classification of Diseases(ICD) in Chinese, with the goal to facilitate automated medical coding in China. METHODS: We applied the entity-linking framework to normalize Chinese diagnosis and procedure terms, which consists of two steps - candidate concept generation and candidate concept ranking. For candidate concept generation, we implemented both the traditional BM25 algorithm and an extended version that integrates a synonym knowledgebase. For candidate concept ranking, we investigated a number of different algorithms: (1) the BM25 algorithm, (2) ranking support vector machines (RankSVM), (3) a previously reported Convolutional Neural Network (CNN) approach, (4) 11 deep ranking-based methods from the MatchZoo toolkit, and (5) a new BERT (Bidirectional Encoder Representations from Transformers) based ranking method. Using two manually annotated datasets (8,547 diagnoses and 8,282 procedures) collected from a Tier 3A hospital in China, we evaluated above methods and reported their performance (i.e., accuracy) at different cutoffs. RESULTS: The coverage of candidate concept generation was greatly improved after integrating the synonym knowledgebase, achieving 97.9% for diagnoses and 93.4% for procedures respectively. Overall the new BERT-based ranking method achieved the best performance on both diagnosis and procedure normalization, with the best accuracy of 92.1% for diagnosis and 80.1% for procedure, when the top one concept and exact match criteria were used. CONCLUSIONS: This study developed and compared diverse entity-linking methods to normalize clinical terms in Chinese and our evaluation shows good performance on mapping disease terms to ICD codes, demonstrating the feasibility of automated encoding of clinical terms in Chinese.

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.

BACKGROUND: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. OBJECTIVE: This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. METHODS: We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. RESULTS: The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. CONCLUSIONS: The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual's level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

Selinexor-induced thrombocytopenia results from inhibition of thrombopoietin signaling in early megakaryopoiesis.

Selinexor is the first oral selective inhibitor of nuclear export compound tested for cancer treatment. Selinexor has demonstrated a safety therapy profile with broad antitumor activity against solid and hematological malignancies in phases 2 and 3 clinical trials (#NCT03071276, #NCT02343042, #NCT02227251, #NCT03110562, and #NCT02606461). Although selinexor shows promising efficacy, its primary adverse effect is high-grade thrombocytopenia. Therefore, we aimed to identify the mechanism of selinexor-induced thrombocytopenia to relieve it and improve its clinical management. We determined that selinexor causes thrombocytopenia by blocking thrombopoietin (TPO) signaling and therefore differentiation of stem cells into megakaryocytes. We then used both in vitro and in vivo models and patient samples to show that selinexor-induced thrombocytopenia is indeed reversible when TPO agonists are administered in the absence of selinexor (drug holiday). In sum, these data reveal (1) the mechanism of selinexor-induced thrombocytopenia, (2) an effective way to reverse the dose-limiting thrombocytopenia, and (3) a novel role for XPO1 in megakaryopoiesis. The improved selinexor dosing regimen described herein is crucial to help reduce thrombocytopenia in selinexor patients, allowing them to continue their course of chemotherapy and have the best chance of survival. This trial was registered at www.clinicaltrials.gov as #NCT01607905.

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text.

BACKGROUND: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. METHODS: A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. RESULTS: Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. CONCLUSIONS: This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

Synthesis and dephosphorylation of MARCKS in the late stages of megakaryocyte maturation drive proplatelet formation.

Platelets are essential for hemostasis, and thrombocytopenia is a major clinical problem. Megakaryocytes (MKs) generate platelets by extending long processes, proplatelets, into sinusoidal blood vessels. However, very little is known about what regulates proplatelet formation. To uncover which proteins were dynamically changing during this process, we compared the proteome and transcriptome of round vs proplatelet-producing MKs by 2D difference gel electrophoresis (DIGE) and polysome profiling, respectively. Our data revealed a significant increase in a poorly-characterized MK protein, myristoylated alanine-rich C-kinase substrate (MARCKS), which was upregulated 3.4- and 5.7-fold in proplatelet-producing MKs in 2D DIGE and polysome profiling analyses, respectively. MARCKS is a protein kinase C (PKC) substrate that binds PIP2. In MKs, it localized to both the plasma and demarcation membranes. MARCKS inhibition by peptide significantly decreased proplatelet formation 53%. To examine the role of MARCKS in the PKC pathway, we treated MKs with polymethacrylate (PMA), which markedly increased MARCKS phosphorylation while significantly inhibiting proplatelet formation 84%, suggesting that MARCKS phosphorylation reduces proplatelet formation. We hypothesized that MARCKS phosphorylation promotes Arp2/3 phosphorylation, which subsequently downregulates proplatelet formation; both MARCKS and Arp2 were dephosphorylated in MKs making proplatelets, and Arp2 inhibition enhanced proplatelet formation. Finally, we used MARCKS knockout (KO) mice to probe the direct role of MARCKS in proplatelet formation; MARCKS KO MKs displayed significantly decreased proplatelet levels. MARCKS expression and signaling in primary MKs is a novel finding. We propose that MARCKS acts as a "molecular switch," binding to and regulating PIP2 signaling to regulate processes like proplatelet extension (microtubule-driven) vs proplatelet branching (Arp2/3 and actin polymerization-driven).

CCL5 derived from platelets increases megakaryocyte proplatelet formation.

In times of physiological stress, platelet count can transiently rise. What initiates this reactive thrombocytosis is poorly understood. Intriguingly, we found that treating megakaryocytes (MKs) with the releasate from activated platelets increased proplatelet production by 47%. Platelets store inflammatory cytokines, including the chemokine ligand 5 (CCL5, RANTES); after TRAP activation, platelets release over 25 ng/mL CCL5. We hypothesized that CCL5 could regulate platelet production by binding to its receptor, CCR5, on MKs. Maraviroc (CCR5 antagonist) or CCL5 immunodepletion diminished 95% and 70% of the effect of platelet releasate, respectively, suggesting CCL5 derived from platelets is sufficient to drive increased platelet production through MK CCR5. MKs cultured with recombinant CCL5 increased proplatelet production by 50% and had significantly higher ploidy. Pretreating the MK cultures with maraviroc prior to exposure to CCL5 reversed the augmented proplatelet formation and ploidy, suggesting that CCL5 increases MK ploidy and proplatelet formation in a CCR5-dependent manner. Interrogation of the Akt signaling pathway suggested that CCL5/CCR5 may influence proplatelet production by suppressing apoptosis. In an in vivo murine acute colitis model, platelet count significantly correlated with inflammation whereas maraviroc treatment abolished this correlation. We propose that CCL5 signaling through CCR5 may increase platelet counts during physiological stress.

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health records (EHRs), typically comes with timestamp information. In this work, we define event sequences and their properties: synchronicity, evenness, and co-cardinality; we then show how asynchronous, uneven, and multi-cardinal problem settings can support explicit accountings of relative time. Our evaluation uses the temporally sensitive clinical use case of pediatric asthma, which is a chronic disease with symptoms (and lack thereof) evolving over time. We show several approaches to explicitly incorporating relative time into a recurrent neural network (RNN) model that improve the overall classification of patients into those with no asthma, those with persistent asthma, those in long-term remission, and those who have experienced relapse. We also compare and contrast these results with those in an inpatient intensive care setting.

Microtubule sliding drives proplatelet elongation and is dependent on cytoplasmic dynein.

Bone marrow megakaryocytes produce platelets by extending long cytoplasmic protrusions, designated proplatelets, into sinusoidal blood vessels. Although microtubules are known to regulate platelet production, the underlying mechanism of proplatelet elongation has yet to be resolved. Here we report that proplatelet formation is a process that can be divided into repetitive phases (extension, pause, and retraction), as revealed by differential interference contrast and fluorescence loss after photoconversion time-lapse microscopy. Furthermore, we show that microtubule sliding drives proplatelet elongation and is dependent on cytoplasmic dynein under static and physiological shear stress by using fluorescence recovery after photobleaching in proplatelets with fluorescence-tagged ß1-tubulin. A refined understanding of the specific mechanisms regulating platelet production will yield strategies to treat patients with thrombocythemia or thrombocytopenia.

Association of a common variant of SYNPO2 gene with increased risk of serous epithelial ovarian cancer.

In China, the majority of ovarian cancer patients (80%-90%) are women who are diagnosed with epithelial ovarian cancer. The SYNPO2 gene has recently been reported to be associated with epithelial ovarian cancer in Europeans. To investigate the association of common variants of SYNPO2 gene with epithelial ovarian cancer in Han Chinese individuals, we designed a case-control study with 719 epithelial ovarian cancer patients and 1568 unrelated healthy controls of Han Chinese descent. A total of 49 tagging single-nucleotide polymorphisms were genotyped; single-single-nucleotide polymorphism association, imputation, and haplotypic association analyses were performed. The single-nucleotide polymorphism rs17329882 was found to be strongly associated with serous epithelial ovarian cancer and with ages ≤49 years, consistent with the pre-menopausal status of analyzed epithelial ovarian cancer cases. Odds ratios and 95% confidence intervals provided evidence of the risk effects of the C allele of the single-nucleotide polymorphism on epithelial ovarian cancer. Imputation analyses also confirmed the results with a similar pattern. Additionally, haplotype analyses indicated that the haplotype block that contained rs17329882 was significantly associated with epithelial ovarian cancer risk, specifically with the serous epithelial ovarian cancer subtype. In conclusion, our results show that SYNPO2 gene plays an important role in the etiology of epithelial ovarian cancer, suggesting that this gene may be a potential genetic modifier for developing epithelial ovarian cancer.

Enhancing fatty acid production in Escherichia coli by Vitreoscilla hemoglobin overexpression.

Our recent 13 C-metabolic flux analysis (13 C-MFA) study indicates that energy metabolism becomes a rate-limiting factor for fatty acid overproduction in E. coli strains (after "Push-Pull-Block" based genetic modifications). To resolve this bottleneck, Vitreoscilla hemoglobin (VHb, a membrane protein facilitating O2 transport) was introduced into a fatty-acid-producing strain to promote oxygen supply and energy metabolism. The resulting strain, FAV50, achieved 70% percent higher fatty acid titer than the parent strain in micro-aerobic shake tube cultures. In high cell-density bioreactor fermentations, FAV50 achieved free fatty acids at a titer of 7.02 g/L (51% of the theoretical yield). In addition to "Push-Pull-Block-Power" strategies, our experiments and flux balance analysis also revealed the fatty acid over-producing strain is sensitive to metabolic burden and oxygen influx, and thus a careful evaluation of the cost-benefit tradeoff with the guidance of fluxome analysis will be fundamental for the rational design of synthetic biology strains. Biotechnol. Bioeng. 2017;114: 463-467. © 2016 Wiley Periodicals, Inc.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism.

BACKGROUND: Flux analyses, including flux balance analysis (FBA) and 13C-metabolic flux analysis (13C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the 13C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the 13C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB®. RESULTS: We constructed an open-source platform for steady-state 13C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify 13C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model's applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results. CONCLUSION: Open-source software for 13C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org /or ( http://tang.eece.wustl.edu/ToolDevelopment.htm ). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.

Platelet bioreactor-on-a-chip.

Platelet transfusions total >2.17 million apheresis-equivalent units per year in the United States and are derived entirely from human donors, despite clinically significant immunogenicity, associated risk of sepsis, and inventory shortages due to high demand and 5-day shelf life. To take advantage of known physiological drivers of thrombopoiesis, we have developed a microfluidic human platelet bioreactor that recapitulates bone marrow stiffness, extracellular matrix composition,micro-channel size, hemodynamic vascular shear stress, and endothelial cell contacts, and it supports high-resolution live-cell microscopy and quantification of platelet production. Physiological shear stresses triggered proplatelet initiation, reproduced ex vivo bone marrow proplatelet production, and generated functional platelets. Modeling human bone marrow composition and hemodynamics in vitro obviates risks associated with platelet procurement and storage to help meet growing transfusion needs.

Antiferromagnetic Spin Seebeck Effect.

We report on the observation of the spin Seebeck effect in antiferromagnetic MnF_{2}. A device scale on-chip heater is deposited on a bilayer of MnF_{2} (110) (30 nm)/Pt (4 nm) grown by molecular beam epitaxy on a MgF_{2} (110) substrate. Using Pt as a spin detector layer, it is possible to measure the thermally generated spin current from MnF_{2} through the inverse spin Hall effect. The low temperature (2-80 K) and high magnetic field (up to 140 kOe) regime is explored. A clear spin-flop transition corresponding to the sudden rotation of antiferromagnetic spins out of the easy axis is observed in the spin Seebeck signal when large magnetic fields (>9 T) are applied parallel to the easy axis of the MnF_{2} thin film. When the magnetic field is applied perpendicular to the easy axis, the spin-flop transition is absent, as expected.