Biological characteristics of Cordyceps militaris single mating-type strains.

Cordyceps militaris has been extensively cultivated as a model cordyceps species for commercial purposes. Nevertheless, the problems related to strain degeneration and breeding technologies remain unresolved. This study assessed the physiology and fertility traits of six C. militaris strains with distinct origins and characteristics, focusing on single mating-type strains. The results demonstrated that the three identified strains (CMDB01, CMSY01, and CMJB02) were single mating-type possessing only one mating-type gene (MAT1-1). In contrast, the other three strains (CMXF07, CMXF09, and CMMS05) were the dual mating type. The MAT1-1 strains sourced from CMDB01, CMSY01, and CMJB02 consistently produced sporocarps but failed to generate ascospores. However, when paired with MAT1-2 strains, the MAT1-1 strains with slender fruiting bodies and normal morphology were fertile. The hyphal growth rate of single mating-type strains (CMDB01, CMSY01, and CMJB02) typically surpassed that of dual mating-type strains (CMXF07, CMXF09, and CMMS05). The growth rates of MAT1-2 and MAT1-1 strains were proportional to their ratios, such that a single mating-type strain with a higher ratio exhibited an increased growth rate. As C. militaris matured, the adenosine content decreased. In summary, the C. militaris strains that consistently produce sporocarps and have a single mating type are highly promising for production and breeding.

Cellulose phosphonate/polyethyleneimine nano-porous composite remove toxic Pb(II) and Cu(II) from water in a short time.

Current cellulose-based adsorbents suffer from the drawbacks of low adsorption capacity or slow adsorption rate for heavy metal ions. It is imperative to prepare new cellulose-based materials to improve the adsorption ability. In this work, we aim to introduce phosphonate groups to improve the adsorption ability of cellulose and select polyethyleneimine (PEI) for synergistic adsorption. A novel cellulose phosphonate/polyethyleneimine composite (MCCP-PEI) is prepared via the Mannich reaction. The structure and composition of MCCP-PEI are characterized by various advanced microscopy and spectroscopy techniques, and the results show that MCCP-PEI possesses abundant nano-porous structure, strong chelating sites, and excellent hydrophilicity. Besides, the adsorption behavior of MCCP-PEI for heavy metals has been systematically investigated. The results show that the adsorbent can quickly remove toxic Cu(II) and Pb(II) from water within 15 min and 20 min, respectively. The saturated adsorption capacity for Cu(II) and Pb(II) is 250.0 and 534.7 mg·g-1, respectively. X-ray photoelectron spectroscopy analysis combined with Density Functional Theory calculations reveal that the adsorption mechanism is chemical complexation and electrostatic attraction, and the phosphonate group plays a key role in the adsorption process.

Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics.

Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.

Structure-preserving integrated analysis for risk stratification with application to cancer staging.

To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and clinical outcomes. A well-known example is the American Joint Committee on Cancer staging for cancer that uses tumor size, node involvement, and metastasis status. We consider a statistical method for such grouping based on individual patient data from multiple studies. The method encourages a common grouping structure as a basis for borrowing information, but acknowledges data heterogeneity including unbalanced data structures across multiple studies. We build on the "lasso-tree" method that is more versatile than the well-known classification and regression tree method in generating possible grouping patterns. In addition, the parametrization of the lasso-tree method makes it very natural to incorporate the underlying order information in the risk factors. In this article, we also strengthen the lasso-tree method by establishing its theoretical properties for which Lin and others (2013. Lasso tree for cancer staging with survival data. Biostatistics 14, 327-339) did not pursue. We evaluate our method in extensive simulation studies and an analysis of multiple breast cancer data sets.

Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding.

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

Performance of a Deep Learning Model vs Human Reviewers in Grading Endoscopic Disease Severity of Patients With Ulcerative Colitis.

Importance: Assessing endoscopic disease severity in ulcerative colitis (UC) is a key element in determining therapeutic response, but its use in clinical practice is limited by the requirement for experienced human reviewers. Objective: To determine whether deep learning models can grade the endoscopic severity of UC as well as experienced human reviewers. Design, Setting, and Participants: In this diagnostic study, retrospective grading of endoscopic images using the 4-level Mayo subscore was performed by 2 independent reviewers with score discrepancies adjudicated by a third reviewer. Using 16 514 images from 3082 patients with UC who underwent colonoscopy at a single tertiary care referral center in the United States between January 1, 2007, and December 31, 2017, a 159-layer convolutional neural network (CNN) was constructed as a deep learning model to train and categorize images into 2 clinically relevant groups: remission (Mayo subscore 0 or 1) and moderate to severe disease (Mayo subscore, 2 or 3). Ninety percent of the cohort was used to build the model and 10% was used to test it; the process was repeated 10 times. A set of 30 full-motion colonoscopy videos, unseen by the model, was then used for external validation to mimic real-world application. Main Outcomes and Measures: Model performance was assessed using area under the receiver operating curve (AUROC), sensitivity and specificity, positive predictive value (PPV), and negative predictive value (NPV). Kappa statistics (κ) were used to measure agreement of the CNN relative to adjudicated human reference cores. Results: The authors included 16 514 images from 3082 unique patients (median [IQR] age, 41.3 [26.1-61.8] years, 1678 [54.4%] female), with 3980 images (24.1%) classified as moderate-to-severe disease by the adjudicated reference score. The CNN was excellent for distinguishing endoscopic remission from moderate-to-severe disease with an AUROC of 0.966 (95% CI, 0.967-0.972); a PPV of 0.87 (95% CI, 0.85-0.88) with a sensitivity of 83.0% (95% CI, 80.8%-85.4%) and specificty of 96.0% (95% CI, 95.1%-97.1%); and NPV of 0.94 (95% CI, 0.93-0.95). Weighted κ agreement between the CNN and the adjudicated reference score was also good for identifying exact Mayo subscores (κ = 0.84; 95% CI, 0.83-0.86) and was similar to the agreement between experienced reviewers (κ = 0.86; 95% CI, 0.85-0.87). Applying the CNN to entire colonoscopy videos had similar accuracy for identifying moderate to severe disease (AUROC, 0.97; 95% CI, 0.963-0.969). Conclusions and Relevance: This study found that deep learning model performance was similar to experienced human reviewers in grading endoscopic severity of UC. Given its scalability, this approach could improve the use of colonoscopy for UC in both research and routine practice.

Measurement of Non-Stationary Characteristics of a Landfall Typhoon at the Jiangyin Bridge Site.

The wind-sensitive long-span suspension bridge is a vital element in land transportation. Understanding the wind characteristics at the bridge site is thus of great significance to the wind- resistant analysis of such a flexible structure. In this study, a strong wind event from a landfall typhoon called Soudelor recorded at the Jiangyin Bridge site with the anemometer is taken as the research object. As inherent time-varying trends are frequently captured in typhoon events, the wind characteristics of Soudelor are analyzed in a non-stationary perspective. The time-varying mean is first extracted with the wavelet-based self-adaptive method. Then, the non-stationary turbulent wind characteristics, e.g.; turbulence intensity, gust factor, turbulence integral scale, and power spectral density, are investigated and compared with the results from the stationary analysis. The comparison highlights the importance of non-stationary considerations of typhoon events, and a transition from stationarity to non-stationarity for the analysis of wind effects. The analytical results could help enrich the database of non-stationary wind characteristics, and are expected to provide references for the wind-resistant analysis of engineering structures in similar areas.

Guillain-Barré syndrome and severe infection following chemotherapy for peripheral T-cell lymphoma: A case report.

Guillain-Barré syndrome (GBS) is a rare complication of malignant lymphoma. The current study describes a case of GBS in a patient with peripheral T-cell lymphoma not otherwise specified (PTCL-NOS). A 47-year-old male was admitted to the First Affiliated Hospital of Zhengzhou University (Zhengzhou, China) with systemic multiple subcutaneous nodules and was diagnosed with stage IV high-grade PTCL-NOS (according to the Revised European American Lymphoma Classification). During chemotherapy, severe infection and progressive flaccid quadriparesis appeared, which eventually developed to respiratory muscles paralysis. The clinical course and neurological examination were consistent with GBS. Following mechanical ventilation and intravenous immunoglobulin administration, the neurological symptoms were in remission after one month. Three months later, the patient achieved complete remission without any treatment during this period. We hypothesized that immune reconstruction may have a significant role in this phenomenon.

Quench dynamics of the anisotropic Heisenberg model.

We develop an analytical approach for the study of the quench dynamics of the anisotropic Heisenberg model (XXZ model) on the infinite line. We present the exact time-dependent wave functions after a quench in an integral form for any initial state and for any anisotropy Δ by means of a generalized Yudson contour representation. We calculate the evolution of several observables from two particular initial states: starting from a local Néel state we calculate the time evolution of the antiferromagnetic order parameter-staggered magnetization; starting from a state with consecutive flipped spins (1) we calculate the evolution of the local magnetization and express it in terms of the propagation of magnons and bound state excitations, and (2) we predict the evolution of the induced spin currents. These predictions can be confronted with experiments in ultracold gases in optical lattices. We also show how the "string" solutions of Bethe ansatz equations emerge naturally from the contour approach.

Study on the dispersion of charged single-wall carbon nanotube bundles by first principles calculation.

We have investigated the dispersion of charged single-wall carbon nanotube bundles by using density functional theory. We obtained the variation of equilibrium spacing between tubes as a function of charge density for the (4, 4) and (7, 0) tubes with different charge signs and the minimum charge density to cause separation. We also calculated the cohesive energies between two charged tubes as a function of interwall spacing and found that extra energy supply can promote separation of nanotube bundle. Our results are in good agreement with the experimental values.