Food anaphylaxis diagnostic marker compilation in machine learning design and validation.

BackgroundTraditional food allergy assessment of anaphylaxis remains limited in accuracy and accessibility. Current methods of anaphylaxis risk assessment are costly with low predictive accuracy. The Tolerance Induction Program (TIP) for anaphylactic patients undergoing TIP immunotherapy produced large-scale diagnostic data across biosimilar proteins, which was used to develop a machine learning model for patient-specific and allergen-specific anaphylaxis assessment. In explanation of construct, this work describes the algorithm design for assignment of peanut allergen score as a quantitative measure of anaphylaxis risk. Secondarily, it confirms the accuracy of the machine learning model for a specific cohort of food anaphylactic children.Methods and resultsMachine learning model design for allergen score prediction utilized 241 individual allergy assays per patient. Accumulation of data across total IgE subdivision served as the basis of data organization. Two regression based Generalized Linear Models (GLM) were utilized to position allergy assessment on a linear scale. The initial model was further tested with sequential patient data over time. A Bayesian method was then used to improve outcomes by calculating the adaptive weights for the results of the two GLMs of peanut allergy score prediction. A linear combination of both provided the final hybrid machine learning prediction algorithm. Specific analysis of peanut anaphylaxis within one endotype model is estimated to predict the severity of possible anaphylactic reaction to peanut with a recall of 95.2% on a dataset of 530 juvenile patients with various food allergies, including but not limited to peanut allergy. Receiver Operating Characteristic analysis yielded over 99% AUC (area under curve) results within peanut allergy prediction.ConclusionsMachine learning algorithm design established from comprehensive molecular allergy data produces high accuracy and recall in anaphylaxis risk assessment. Subsequent design of additional food protein anaphylaxis algorithms is needed to improve the precision and efficiency of clinical food allergy assessment and immunotherapy treatment

A strategy for reducing gross errors in the generalized Born models of implicit solvation

The “canonical” generalized Born (GB) formula [C. Still, A. Tempczyk, R. C. Hawley, and T. Hendrickson, J. Am. Chem. Soc. 112, 6127 (1990)] is known to provide accurate estimates for total electrostatic solvation energies ΔGel of biomolecules if the corresponding effective Born radii are accurate. Here we show that even if the effective Born radii are perfectly accurate, the canonical formula still exhibits significant number of gross errors (errors larger than 2kBT relative to numerical Poisson equation reference) in pairwise interactions between individual atomic charges. Analysis of exact analytical solutions of the Poisson equation (PE) for several idealized nonspherical geometries reveals two distinct spatial modes of the PE solution; these modes are also found in realistic biomolecular shapes. The canonical GB Green function misses one of two modes seen in the exact PE solution, which explains the observed gross errors. To address the problem and reduce gross errors of the GB formalism, we have used exact PE solutions for idealized nonspherical geometries to suggest an alternative analytical Green function to replace the canonical GB formula. The proposed functional form is mathematically nearly as simple as the original, but depends not only on the effective Born radii but also on their gradients, which allows for better representation of details of nonspherical molecular shapes. In particular, the proposed functional form captures both modes of the PE solution seen in nonspherical geometries. Tests on realistic biomolecular structures ranging from small peptides to medium size proteins show that the proposed functional form reduces gross pairwise errors in all cases, with the amount of reduction varying from more than an order of magnitude for small structures to a factor of 2 for the largest ones

Three-dimensional Switchable Photonic Crystals via Various Optical Wave Interference Techniques

A theoretical study has been undertaken to elucidate the three‐dimensional pattern formation during holographic polymer‐dispersed liquid crystal fabrication employing various optical wave interference techniques. Initially miscible mixtures of nematic liquid crystal and reactive multifunctional monomer with a photosensitive initiator were exposed to geometrically arranged interfering beams of light, producing a spatially dependent intensity distribution within the sample. To mimic the spatio‐temporal evolution of periodic photonic structures in three dimensions, the time‐dependent Ginzburg–Landau Model C equations, coupled with spatially variant reaction rate equations, have been solved numerically incorporating the local free energy densities pertaining to isotropic mixing, nematic ordering, and network elasticity. The simulated results reveal some key observations during the formation of electrically switchable photonic crystals with few defects. It appears that the network elasticity term exerts profound effects on resultant structures, indicating that photonic crystals with fewer point defects may be fabricated in shorter times. The simulated results are in good qualitative agreement with reported experimental observations in respect of emerged patterns, length and time scales

A novel approach based on spanning probe microscopy for nanolithography in polymer films

See Lin

Electrostatic nanolithography in polymers using atomic force microscopy

The past decade has witnessed an explosion of techniques used to pattern polymers on the nano (1–100 nm) and submicrometre (100–1,000 nm) scale, driven by the extensive versatility of polymers for diverse applications, such as molecular electronics1, 2, data storage3, optoelectronics4, displays5, sacrificial templates6, 7 and all forms of sensors. Conceptually, most of the patterning techniques, including microcontact printing (soft lithography)8, photolithography9, 10, electron-beam lithography11, block-copolymer templating12, 13 and dip-pen lithography14, are based on the spatially selective removal or formation/deposition of polymer. Here, we demonstrate an alternative and novel lithography technique—electrostatic nanolithography using atomic force microscopy—that generates features by mass transport of polymer within an initially uniform, planar film without chemical crosslinking, substantial polymer degradation or ablation. The combination of localized softening of attolitres (102–105 nm3) of polymer by Joule heating, extremely non-uniform electric field gradients to polarize and manipulate the soften polymer, and single-step process methodology using conventional atomic force microscopy (AFM) equipment, establishes a new paradigm for polymer nanolithography, allowing rapid (of the order of milliseconds) creation of raised (or depressed) features without external heating of a polymer film or AFM tip–film contact