PURPOSE:The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS:We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS:For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date.

In dedicated wood pellet combustion and cofiring with coal in large pulverized fuel furnaces, poor grindability and low bulk density of biomass are important issues for lowering the unburned carbon in ash and achieving high cofiring ratios with coal for pulverized fuel combustion furnaces. In this study, the torrefaction of wood pellets was investigated for improvement of energy density and grindability. The torrefaction tests were performed using a fixed bed reactor for a temperature range of 210–310 °C and holding time of 15–60 min. The mass yield varied from 86.18 to 39.46% accompanied by an increase in the carbon content and heating value. The properties of torrefied wood pellets (TWP) were correlated with the mass yield for use with different time–temperature histories. The bulk density decreased by the mass yield rose to a power of 0.538. The energy density of TWP was higher in the initial torrefaction stage with a peak of 10.41 GJ/m3 but was below that for the original pellets when the mass yield was approximately ≤60%. The grindability of TWP increased almost linearly with the degree of torrefaction, and the mass yield of 80% attained the lower range of the grindability of coal.

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We have engineered a panel of novel Fn3 scaffold-based proteins that bind with high specificity and affinity to each of the individual mouse Fcγ receptors (mFcγR). These binders were expressed as fusions to anti-tumor antigen single-chain antibodies and mouse serum albumin, creating opsonizing agents that invoke only a single mFcγR response rather than the broader activity of natural Fc isotypes, as well as all previously reported Fc mutants. This panel isolated the capability of each of the four mFcγRs to contribute to macrophage phagocytosis of opsonized tumor cells and in vivo tumor growth control with these monospecific opsonizing fusion proteins. All activating receptors (mFcγRI, mFcγRIII, and mFcγRIV) were capable of driving specific tumor cell phagocytosis to an equivalent extent, while mFcγRII, the inhibitory receptor, did not drive phagocytosis. Monospecific opsonizing fusion proteins that bound mFcγRI alone controlled tumor growth to an extent similar to the most active IgG2a murine isotype. As expected, binding to the inhibitory mFcγRII did not delay tumor growth, but unexpectedly, mFcγRIII also failed to control tumor growth. mFcγRIV exhibited detectable but lesser tumor-growth control leading to less overall survival compared to mFcγRI. Interestingly, in vivo macrophage depletion demonstrates their importance in tumor control with mFcγRIV engagement, but not with mFcγRI. This panel of monospecific mFcγR-binding proteins provides a toolkit for isolating the functional effects of each mFcγR in the context of an intact immune system.

Summary Torrefaction of biomass improves the fuel quality via mild thermal decomposition of the lignocellulosic structure. Establishing common relationships for key characteristics of various types of torrefied biomass can benefit the process design and reaction severity assessment of samples in a commercial plant. In this study, the properties of torrefied biomass were correlated with the mass yield for the experimental results of five biomass samples (namely, wood chips, wood pellets, kenaf, rice straw, and rice husk) processed between 190°C and 310°C with a mass loss of up to 57.0% on a dry, ash‐free (daf) basis. Compared with existing studies, improved curve‐fit quality (R2 = 0.954‐0.994) was achieved in terms of the volatile matter/fixed carbon ratio, elemental composition, and heating value based on an assessment of the property ratio between raw and torrefied biomass for the mass yield on a daf basis. A new methodology was proposed to derive the torrefaction severity from the pyrolysis characteristics using thermogravimetric analysis, with R2 = 0.951. Additionally, the amount of fixed carbon increased by up to 44% compared to that of raw biomass during torrefaction, indicating cross‐linking and polymerization reactions. The energy density of torrefied biomass varies widely between 1.7 and 10.4 GJ/m3 depending on the compaction degree of the raw biomass and torrefaction severity. The grindability of torrefied biomass was similar to coal when the mass loss reached 13% daf for woody samples and kenaf.

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