Hierarchical Multicriteria Optimization of Molecular Models of Water.

Many widely used molecular models of water are built from a single Lennard-Jones site on which three point charges are positioned, one negative and two positive ones. Models from that class, denoted LJ3PC here, are computationally efficient, but it is well known that they cannot represent all relevant properties of water simultaneously with good accuracy. Despite the importance of the LJ3PC water model class, its inherent limitations in simultaneously describing different properties of water have never been studied systematically. This task can only be solved by multicriteria optimization (MCO). However, due to its computational cost, applying MCO to molecular models is a formidable task. We have recently introduced the reduced units method (RUM) to cope with this problem. In the present work, we apply the RUM in a hierarchical scheme to optimize LJ3PC water models taking into account five objectives: the representation of vapor pressure, saturated liquid density, self-diffusion coefficient, shear viscosity, and relative permittivity. Of the six parameters of the LJ3PC models, five were varied; only the H-O-H bond angle, which is usually chosen based on physical arguments, was kept constant. Our hierarchical RUM-based approach yields a Pareto set that contains attractive new water models. Furthermore, the results give an idea of what can be achieved by molecular modeling of water with models from the LJ3PC class.

Simulation and optimization of nutrient uptake and biomass formation using a multi-parameter Monod-type model of tobacco BY-2 cell suspension cultures in a stirred-tank bioreactor.

Introduction: Tobacco (Nicotiana tabacum) cv Bright Yellow-2 (BY-2) cell suspension cultures enable the rapid production of complex protein-based biopharmaceuticals but currently achieve low volumetric productivity due to slow biomass formation. The biomass yield can be improved with tailored media, which can be designed either by laborious trial-and-error experiments or systematic, rational design using mechanistic models, linking nutrient consumption and biomass formation. Methods: Here we developed an iterative experiment-modeling-optimization workflow to gradually refine such a model and its predictions, based on collected data concerning BY-2 cell macronutrient consumption (sucrose, ammonium, nitrate and phosphate) and biomass formation. Results and discussion: The biomass formation was well predicted by an unstructured segregated mechanistic Monod-type model as long as the nutrient concentrations did not approach zero (we omitted phosphate, which was completely depleted). Multi-criteria optimization for sucrose and biomass formation indicated the best tradeoff (in a Paretian sense) between maximum biomass yield and minimum process time by reducing the initial sucrose concentration, whereas the inoculation biomass could be increased to maximize the biomass yield or minimize the process time, which we confirmed in calibration experiments. The model became inaccurate at biomass densities > 8 g L-1 dry mass when sucrose was almost depleted. We compensated for this limitation by including glucose and fructose as sucrose hydrolysis products in the model. The remaining offset between the simulation and experimental data might be resolved by including intracellular pools of sucrose, ammonium, nitrate and phosphate. Overall, we demonstrated that iterative models can be used to systematically optimize conditions for bioreactor-based processes.

An optimization case study for solving a transport robot scheduling problem on quantum-hybrid and quantum-inspired hardware.

We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem. This problem originates from an industrially relevant real-world scenario. We provide three different models for our problem following different design philosophies. In our benchmark, we focus on the solution quality and end-to-end runtime of different model and solver combinations. We find promising results for the digital annealer and some opportunities for the hybrid quantum annealer in direct comparison with Gurobi. Our study provides insights into the workflow for solving an application-oriented optimization problem with different strategies, and can be useful for evaluating the strengths and weaknesses of different approaches.

Calibrated simplex-mapping classification.

We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n - 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.

Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions.

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies, as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machine-learning framework on experimental data for activity coefficients in binary systems of 1146 components from the Dortmund Data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multicomponent systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model.

Spontaneous parametric down-conversion of photons at 660 nm to the terahertz and sub-terahertz frequency range: erratum.

In this erratum, we correct two typing errors from our previously published manuscript [Opt. Express27, 7458 (2019)10.1364/OE.27.007458]. In the original manuscript, the two errors were limited to the theoretical derivation and did not touch the numerical calculations such that the results and conclusions remain unchanged.

Radioactive iodine-125 seed localization as an aid in reoperative neck surgery.

BACKGROUND: Scarring and disrupted tissue planes add to already-complex neck anatomy and make localization of nonpalpable pathology difficult in cervical endocrine reoperations. We describe the use of radioactive iodine-125 seed localization (RSL) in 6 patients with metastatic papillary thyroid carcinoma (PTC) and 2 with recurrent hyperparathyroidism. METHODS: Eight patients had 2-D ultrasound-guided RSL of the target lesion, 0-3 days preoperatively. Intraoperative gamma probe (Neoprobe) was used to plan incision placement and localize the implanted seed. Recorded operative variables included: number of lymph nodes (LNs) harvested, estimated blood loss (EBL), operative time, length of stay (LOS) and RSL and operative complications. RESULTS: All patients had successful resection of the targeted area and removal of the radioactive seed. There was no seed migration. Two complications occurred in the thyroid group. CONCLUSION: Radioactive iodine 125 seeds facilitate successful localization of endocrine pathology during reoperative cervical procedures.

Extrathyroidal extension predicts negative clinical outcomes in papillary thyroid cancer.

BACKGROUND: The eighth edition American Joint Committee on Cancer tumor-node-metastasis staging for well-differentiated thyroid cancers, no longer considers "minimal" extrathyroidal extension for tumor staging. This change prompted us to examine the effect of extrathyroidal extension on patient outcomes. METHODS: Patients (n = 177,497) in the 2016 National Cancer Database with classic papillary thyroid cancer were evaluated to determine the effect of extrathyroidal extension on overall survival and risk for nodal and distant metastases. Kaplan-Meier curves with the log-rank test were used to evaluate survival differences. Multivariable Cox and logistic regression analyses included relevant clinicopathologic variables (e.g. age, sex, race, and Charlson Comorbidity Index). RESULTS: Patients with "minimal" extrathyroidal extension had worse survival versus patients with no extrathyroidal extension (10-year survival 89.3% vs 93.1%, hazard ratio 1.23; 95% confidence interval, 1.13-1.35; P < .001). Any extrathyroidal extension was associated with higher risks for lymph node (odds ratio 2.78; 95% confidence interval, 2.69-2.87) and distant metastasis (odds ratio 3.5; 95% confidence interval, 3.05-4.04). These associations persisted when comparing "micro" (extension into the thyroid capsule) versus none for nodal risk (odds ratio 1.25; 95% confidence interval, 1.18-1.33) and distant metastasis (OR 1.52; 95% confidence interval, 1.11-2.09). CONCLUSION: All levels of extrathyroidal extension, including microscopic, were associated with increased risk for nodal and distant metastasis. Both minimal and macroscopic extrathyroidal extension were also associated with decreased overall survival. Such findings have the potential to affect the clinical decision making for patients diagnosed with papillary thyroid cancer.

Multicriteria Optimization of Molecular Models of Water Using a Reduced Units Approach.

Multicriteria optimization (MCO) is used to parametrize molecular models of water. The set of the best possible compromises between different objectives, the Pareto set, is determined. Calculating Pareto sets for optimization problems involving molecular simulations is computationally expensive. Therefore, we use a novel, highly efficient method, which is based on the fact that numerical results from molecular simulations can be interpreted as dimensionless numbers. Hence, they carry information on an entire class of models in physical units. This approach was applied here for the MCO of water models of the "one-center Lennard-Jones + point charge" type, in which the objectives were the quality of the description of the vapor pressure, liquid density, and enthalpy of vaporization. The results were compared to models from the literature. Significant improvements were observed. The new optimization method for the development of molecular models is efficient, robust, and broadly applicable.

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion.

Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physicochemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.

Spontaneous parametric down-conversion of photons at 660 nm to the terahertz and sub-terahertz frequency range.

We report on spontaneous parametric down-conversion (SPDC) in periodically poled lithium niobate (PPLN) using 660 nm pump wavelength and the type 0 phase-matching condition to the terahertz and even sub-terahertz frequency range. Detection of the frequency-shifted signal photons is achieved by using highly efficient and narrowband volume Bragg gratings and an uncooled sCMOS camera. The acquired frequency-angular spectrum shows backward and forward generation of terahertz and sub-terahertz photons by SPDC, as well as up-conversion and higher order quasi phase-matching (QPM). The frequency-angular spectrum is theoretically calculated using a Monte-Carlo integration scheme showing a high agreement with the measurement. This work is one important step toward quantum sensing and imaging in the terahertz and sub-terahertz frequency range.

Same day discharge after thyroidectomy is safe and effective.

Historically, thyroidectomies have been performed as inpatient operations due to concerns of postoperative bleeding and symptomatic hypocalcemia. We aim to demonstrate that outpatient thyroidectomy can be performed safely. METHODS: This report outlines a 7-year retrospective analysis (2009-2016) of outpatient vs inpatient thyroidectomies, with outcomes including hematoma, blood loss, recurrent laryngeal nerve injury, symptomatic hypocalcemia, and postoperative emergency room (ER) visits. RESULTS: A total of 1460 thyroidectomies were performed: 1272 (87%) outpatient and 188 (13%) inpatient. Five outpatients: 4 total thyroidectomies (TT), 1 TT with a central lymph node dissection (CLND), and 1 partial thyroidectomy (PT) developed postoperative hematomas (0.34%) at post-discharge hour 3, 9, 10, 13, and 42. Average time to discharge was 2 hours and 37 minutes. Hematomas were evacuated successfully in the operating room under local anesthesia with a 2-day average hospital stay. There were no differences between TT, thyroid lobectomy (TL), and PT procedures for postoperative hematoma (p=0.17). Outpatient compared to inpatient thyroidectomy was more likely to have been performed in patients with lower American Society of Anesthesia scores (2.3 vs 2.9, p<0.0001), less mean blood loss (74 vs 227 ml, p<0.0001), lesser age (52 vs 56 years, p=0.0012), less extensive dissection (p<0.0001), and fewer RLN injuries (2.4% vs 8.5%, p<0.0001). There was no difference between outpatient and inpatient symptomatic hypocalcemia (6.3% vs 9.6%, p=0.09), 30-day postoperative ER visits (8.8% vs 9.6%, p=0.73), and postoperative hematoma (0.39% vs 0%, p=0.39). There was one inpatient mortality from stroke. CONCLUSION: Postoperative hematomas can be managed safely without life-threatening complications suggesting outpatient thyroidectomy can be performed safely by an experienced surgeon, and adverse sequelae dealt with in a safe and effective manner.

Genome Sequences of Gordonia terrae Bacteriophages Phinally and Vivi2.

Bacteriophages Phinally and Vivi2 were isolated from soil from Pittsburgh, Pennsylvania, USA, using host Gordonia terrae 3612. The Phinally and Vivi2 genomes are 59,265 bp and 59,337 bp, respectively, and share sequence similarity with each other and with GTE6. Fewer than 25% of the 87 to 89 putative genes have predictable functions.

The critical spot eraser-a method to interactively control the correction of local hot and cold spots in IMRT planning.

Common problems in inverse radiotherapy planning are localized dose insufficiencies like hot spots in organs at risk or cold spots inside targets. These are hard to correct since the optimization is based on global evaluations like maximum/minimum doses, equivalent uniform doses or dose-volume constraints for whole structures. In this work, we present a new approach to locally correct the dose of any given treatment plan. Once a treatment plan has been found that is acceptable in general but requires local corrections, these areas are marked by the planner. Then the system generates new plans that fulfil the local dose goals. Consequently, it is possible to interactively explore all plans between the locally corrected plans and the original treatment plan, allowing one to exactly adjust the degree of local correction and how the plan changes overall. Both the amount (in Gy) and the size of the local dose change can be navigated. The method is introduced formally as a new mathematical optimization setting, and is evaluated using a clinical example of a meningioma at the base of the skull. It was possible to eliminate a hot spot outside the target volume while controlling the dose changes to all other parts of the treatment plan. The proposed method has the potential to become the final standard step of inverse treatment planning.

Local density of states for individual energy levels in finite quantum wires.

The local density of states in finite quantum wires is calculated as a function of discrete energies and position along the wire. By using a combination of numerical density matrix renormalization group calculations and analytical bosonization techniques, it is possible to obtain a good understanding of the local spectral weights along the wire in terms of the underlying many-body excitations.