Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic ...subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
A novel nano‐packing material with lower relative humidity, oxygen transmission rate and high longitudinal strength was synthesized by blending polyethylene with nano‐powder (nano‐Ag, kaolin, anatase ...TiO2, rutile TiO2), and its effect on preservation quality of strawberry fruits (Fragaria ananassa Duch. cv Fengxiang) was investigated during storage at 4 °C. Results showed that nano‐packaging was able to maintain the sensory, physicochemical, and physiological quality of strawberry fruits at a higher level compared with the normal packing (polyethylene bags). After a 12‐d storage, decreases in the contents of total soluble solids, titratable acidity, and ascorbic acid of nano‐packing were significantly inhibited. Meanwhile, decay rate, anthocyanin, and malondialdehyde contents were decreased to 16.7%, 26.3 mg/100g, 66.3 μmol/g for nano‐packing and 26.8%, 31.9 mg/100g, 75.4 μmol/g for normal packing; polyphenoloxidase (PPO) and pyrogallol peroxidase (POD) activities were significantly lower in nano‐packing than the control. These data indicated that the nano‐packaging might provide an attractive alternative to improve preservation quality of the strawberry fruits during extended storage.
Practical Application: Nano‐packing exhibited identified quality benefits applicable to the preservation of fresh strawberry. Furthermore, nano‐packing has the advantages of simple processing and feasibility to be industrialized in contrast with other storages. Thus, the utilization of nano‐packing will likely assist commercial producers and retailers in extending the shelf life of products over a broader range in the future.
Here, in an analysis of a 2.92 fb–1 data sample taken at 3.773 GeV with the BESIII detector operated at the BEPCII collider, we measure the absolute decay branching fractions to be B(D0 → K–e+νe) = ...(3.505 ± 0.014 ± 0.033)% and B(D0 → π–e+νe) = (0.295 ± 0.004 ± 0.003)%. From a study of the differential decay rates we obtain the products of hadronic form factor and the magnitude of the CKM matrix element $f$ $^{K}_{+}$(0)|Vcs| = 0.7172 ± 0.0025 ± 0.0035 and $f$ $^{π}_{+}$(0)|Vcd| = 0.1435 ± 0.0018 ± 0.0009.
The biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of ...literature thus represent an important way by which the efficiency of these processes can be improved.
We describe the next generation of the Textpresso information retrieval system, Textpresso Central (TPC). TPC builds on the strengths of the original system by expanding the full text corpus to include the PubMed Central Open Access Subset (PMC OA), as well as the WormBase C. elegans bibliography. In addition, TPC allows users to create a customized corpus by uploading and processing documents of their choosing. TPC is UIMA compliant, to facilitate compatibility with external processing modules, and takes advantage of Lucene indexing and search technology for efficient handling of millions of full text documents. Like Textpresso, TPC searches can be performed using keywords and/or categories (semantically related groups of terms), but to provide better context for interpreting and validating queries, search results may now be viewed as highlighted passages in the context of full text. To facilitate biocuration efforts, TPC also allows users to select text spans from the full text and annotate them, create customized curation forms for any data type, and send resulting annotations to external curation databases. As an example of such a curation form, we describe integration of TPC with the Noctua curation tool developed by the Gene Ontology (GO) Consortium.
Textpresso Central is an online literature search and curation platform that enables biocurators and biomedical researchers to search and mine the full text of literature by integrating keyword and category searches with viewing search results in the context of the full text. It also allows users to create customized curation interfaces, use those interfaces to make annotations linked to supporting evidence statements, and then send those annotations to any database in the world. Textpresso Central URL: http://www.textpresso.org/tpc.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The pedestal structure of the high-confinement mode (H-mode) operation strongly impacts global confinement and fusion performance in tokamak devices. Studies of the pedestal structure also play an ...important role in better understanding the characterization of H-mode plasma discharges with type-I edge localized modes on the Experimental Advanced Superconducting Tokamak (EAST). The EPED model has been widely validated to predict the pedestal structure of several tokamak devices. The mean pedestal width Δψ correlates with the square root of the poloidal pedestal beta βp,ped1/2 and is in good agreement with Δψ=0.12βp,ped1/2 from experimental observations. It is also found that the scaling coefficients of Δψ and βp,ped1/2 may have no dependence on the heating schemes. The comparison of predictions with measurements indicates that the REPED model could be used to predict the pedestal height for a range of experimental pedestal pressures ( 1.7<pped<2.6 kPa) on EAST.
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