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Background: Computed tomography (CT) is the reference standard for assessment of the bone. Magnetic resonance imaging (MRI) developments enable a CT-like visualization of the osseous structures. Purpose: To assess the diagnostic performance of 3D zero-echo time (3D-ZTE) and 3D T1-weighted gradient-echo (3D-T1GRE) MRI sequences for the evaluation of lumbar facet joints (LFJs) and the detection of lumbosacral transitional vertebrae (LSTV) using CT as the reference standard. Material and Methods: In total, 87 adult patients were included in this prospective study. Evaluation of degenerative changes of the facet joints at the L3/L4, L4/L5, and L5/S1 levels on both sides was performed by two readers using a 4-point Likert scale. LSTV were classified according to Castelvi et al. Image quality was quantitatively measured using the signal-to-noise (SNR) and contrast-to-noise (CNR) ratios. Intra-reader, inter-reader, and inter-modality reliability were calculated using Cohen's kappa statistic. Results: Intra-reader agreement for 3D-ZTE, 3D-T1GRE, and CT was 0.607, 0.751, and 0.856 and inter-reader agreement was 0.535, 0.563, and 0.599, respectively. The inter-modality agreement between 3D-ZTE and CT was 0.631 and between 3D-T1GRE and CT 0.665. A total of LSTV were identified in both MR sequences with overall comparable accuracy compared to CT. Mean SNR for bone, muscle, and fat was highest for 3D-T1GRE and mean CNR was highest for CT. Conclusion: 3D-ZTE and 3D-T1GRE MRI sequences can assess the LFJs and LSTV and may serve as potential alternatives to CT. [ABSTRACT FROM AUTHOR]

Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distributionally robust optimization and machine learning communities to formulate and solve data-driven optimization problems with rigorous statistical guarantees. In this technical note we prove that the Wasserstein ball is weakly compact under mild conditions, and we offer necessary and sufficient conditions for the existence of optimal solutions. We also characterize the sparsity of solutions if the Wasserstein ball is centred at a discrete reference measure. In comparison with the existing literature, which has proved similar results under different conditions, our proofs are self-contained and shorter, yet mathematically rigorous, and our necessary and sufficient conditions for the existence of optimal solutions are easily verifiable in practice. [ABSTRACT FROM AUTHOR]

In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming of most existing distributionally robust optimization models is that their ambiguity sets contain pathological discrete distributions that give nature too much freedom to inflict damage. We thus introduce a new class of ambiguity sets that contain only distributions with sum-of-squares (SOS) polynomial density functions of known degrees. We show that these ambiguity sets are highly expressive as they conveniently accommodate distributional information about higher-order moments, conditional probabilities, conditional moments or marginal distributions. Exploiting the theoretical properties of a measure-based hierarchy for polynomial optimization due to Lasserre (SIAM J Optim 21(3):864–885, 2011), we prove that certain worst-case expectation constraints are polynomial-time solvable under these new ambiguity sets. We also show how SOS densities can be used to approximately solve the general problem of moments. We showcase the applicability of the proposed approach in the context of a stylized portfolio optimization problem and a risk aggregation problem of an insurance company. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the ability of endogenous Lactobacillus paracasei ATR6 to grow in unsupplemented bovine cheese whey (CW) and ricotta whey (RW), and to evaluate the efficiency of these media as encapsulating agents for the spray-drying (SD) of this bacterium. Growth assays indicated that both CW and RW are adequate culture media since L. paracasei ATR6 grew satisfactorily (~ 4 log cycles) in both compared to the control medium (skimmed milk). Concerning their potential as encapsulating agents, results showed that survival rates after SD were similar for CW and RW (> 78%), as were the yields (> 47%) of this process. CW allowed the formation of smaller capsules (5.31 ± 1.27 μm) than RW (15.21 ± 7.24 μm), with lower moisture: 8.60% and 13.52%, respectively. Powders were stored at 25 °C for 60 days, during which CW and RW maintained satisfactory bacterial viability. Water activity increased, yet remained below 0.3. Moisture content of CW and RW powders was relatively stable throughout the 60 days; however, values obtained in this study are considered to lie outside the adequate range (4–7%) for product stability. We did not observe color changes in CW and RW microcapsules during storage. Due to its lower protein content, RW offered less protection, which led to higher loss in acidification ability of the strain. CW and RW are suitable culture media and encapsulating agents for the growth of L. paracasei ATR6; however, the former offered better protection, which enabled the maintenance of biological activity. [ABSTRACT FROM AUTHOR]

Lactic acid bacteria (LAB) are applied in the food industry as fermentation agents for a variety of products. Cheese whey (CW) and second cheese whey (SCW) are the two main by-products of cheese production, and can be reused for biotechnological purposes. In this context, this review aims to summarise literature on the use of CW and SCW as culture media for the growth of LAB, as cryoprotectants for freeze-drying and as encapsulating agents for the spray-drying of these microorganisms. CW contains several nutrients that are reported to enhance the growth of LAB. As for SCW, research shows that it has potential to be applied as culture medium, although bacterial growth remains at least one or two log orders lower than CW. Regarding freeze-drying and spray-drying, reports indicate that whey-based cryoprotectants and CW as encapsulating agent offer significant protection from the stressful conditions employed in these processes. [ABSTRACT FROM AUTHOR]

Abstract Campomanesia aurea O. Berg is a native plant of the biome Pampa , which belongs to the family Myrtaceae. The present study aimed to evaluate the antimicrobial and antibiofilme activity of the essential oil of Campomanesia aurea (EOCA) against Listeria monocytogenes ATCC 19114, Staphylococcus aureus ATCC 25923, Salmonella enteritidis ATCC 13076 and Pseudomonas aeruginosa ATCC 27853. An analysis of the chemical composition of the EOCA was realized through gas chromatography coupled to mass spectrometry (GC-MS). The analysis for in vitro evaluation of the antimicrobial and antibiofilm activity was realized by determination of the minimal inhibitory concentration (MIC) and that of the antibiofilm through utilization of 96-well plates with crystal violet, respectively. The action of standard (E) –nerolidol (major compound of the EOCA) was also tested. GC-MS analysis revealed the presence of the sesquiterpene (E)- nerolidol (56.04%) as the main compound in the EOCA. Antimicrobial activity of the EOCA against L. monocytogenes (MIC 5.0 mg mL−1) and S. aureus (MIC 0.7 mg mL−1) was observed. Inhibition of biofilm formation against L. monocytogenes , S. aureus and S. enteritidis could be observed for EOCA and (E)- nerolidol. The results demonstrate that the EOCA was efficient against inhibition of biofilm formation for most of the tested pathogens. Highlights • Essential oil of Campomanesia aurea O. Berg is inhibitor of L. monocytogenes biofilm. • Essential oil of Campomanesia aurea O. Berg can be used against food pathogens. • Essential oil of Campomanesia aurea O. Berg can be a promising source for bioprospection. [ABSTRACT FROM AUTHOR]

We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst-case distribution within this Wasserstein ball. The state-of-the-art methods for solving the resulting distributionally robust optimization problems rely on global optimization techniques, which quickly become computationally excruciating. In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over Wasserstein balls can in fact be reformulated as finite convex programs—in many interesting cases even as tractable linear programs. Leveraging recent measure concentration results, we also show that their solutions enjoy powerful finite-sample performance guarantees. Our theoretical results are exemplified in mean-risk portfolio optimization as well as uncertainty quantification. [ABSTRACT FROM AUTHOR]

This work aimed to validate a method for the quantification of free amino acids in cheese whey protein concentrate and cheese whey protein hydrolysate via gas chromatography coupled with mass spectrometry (GC–MS). For this, free amino acid analysis was carried out with deproteinisation, where the efficiencies of acetonitrile and trichloroacetic acid were compared, prior to derivatisation with N-methyl-N-(tert-butyldimethylsilyl)trifluoroacetamide before GC–MS analysis. The results demonstrated that using trichloroacetic acid enabled the determination of 13 free amino acids (l -alanine, l -valine, l -leucine, l -isoleucine, l -proline, l -methionine, l -serine, l -phenylalanine, l -aspartic acid, l -glutamic acid, l -lysine, l -tyrosine, and l -cystine) in cheese whey protein concentrate and hydrolysate. The method was validated in terms of recovery (85–105%), limits of detection (0.03–0.77 μg mL−1), and limits of quantification (0.09–2.56 μg mL−1). This GC–MS methodology can be used in food laboratories for the quantification of some free amino acids, including branched-chain amino acids, in solid dairy products. [ABSTRACT FROM AUTHOR]

We study two-stage stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see decision. Using modern decision rule approximations, we construct lower bounds on an optimistic version and upper bounds on a pessimistic version of the leader's problem. Both bounding problems are equivalent to explicit mixed-integer linear programs that are amenable to efficient numerical solution. The method is illustrated through a facility location problem involving sellers and customers with con icting preferences. [ABSTRACT FROM AUTHOR]

Decision tree ensembles are among the most robust, high-performing and computationally efficient machine learning approaches for quantitative structure–activity relationship (QSAR) modeling. Among them, gradient boosting has recently garnered particular attention, for its performance in data science competitions, virtual screening campaigns, and bioactivity prediction. However, different variants of gradient boosting exist, the most popular being XGBoost, LightGBM and CatBoost. Our study provides the first comprehensive comparison of these approaches for QSAR. To this end, we trained 157,590 gradient boosting models, which were evaluated on 16 datasets and 94 endpoints, comprising 1.4 million compounds in total. Our results show that XGBoost generally achieves the best predictive performance, while LightGBM requires the least training time, especially for larger datasets. In terms of feature importance, the models surprisingly rank molecular features differently, reflecting differences in regularization techniques and decision tree structures. Thus, expert knowledge must always be employed when evaluating data-driven explanations of bioactivity. Furthermore, our results show that the relevance of each hyperparameter varies greatly across datasets and that it is crucial to optimize as many hyperparameters as possible to maximize the predictive performance. In conclusion, our study provides the first set of guidelines for cheminformatics practitioners to effectively train, optimize and evaluate gradient boosting models for virtual screening and QSAR applications. [ABSTRACT FROM AUTHOR]

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Math Program A 106(3):423-432, 2006). Amongst others, that paper argues that linear two-stage stochastic programs with fixed recourse are #P-hard even if the random problem data is governed by independent uniform distributions. We show that Dyer and Stougie's proof is not correct, and we offer a correction which establishes the stronger result that even the approximate solution of such problems is #P-hard for a sufficiently high accuracy. We also provide new results which indicate that linear two-stage stochastic programs with random recourse seem even more challenging to solve. [ABSTRACT FROM AUTHOR]

We present a risk-averse multi-dimensional newsvendor model for a class of products whose demands are strongly correlated and subject to fashion trends that are not fully understood at the time when orders are placed. The demand distribution is known to be multimodal in the sense that there are spatially separated clusters of probability mass but otherwise lacks a complete description. We assume that the newsvendor hedges against distributional ambiguity by minimizing the worst-case risk of the order portfolio over all distributions that are compatible with the given modality information. We demonstrate that the resulting distributionally robust optimization problem is $$\mathrm{NP}$$ -hard but admits an efficient numerical solution in quadratic decision rules. This approximation is conservative and computationally tractable. Moreover, it achieves a high level of accuracy in numerical tests. We further demonstrate that disregarding ambiguity or multimodality can lead to unstable solutions that perform poorly in stress test experiments. [ABSTRACT FROM AUTHOR]

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, machine learning and computer vision, however, this perception has not yet received a theoretical justification. To fill this gap, we prove that computing the Wasserstein distance between a discrete probability measure supported on two points and the Lebesgue measure on the standard hypercube is already # P-hard. This insight prompts us to seek approximate solutions for semi-discrete optimal transport problems. We thus perturb the underlying transportation cost with an additive disturbance governed by an ambiguous probability distribution, and we introduce a distributionally robust dual optimal transport problem whose objective function is smoothed with the most adverse disturbance distributions from within a given ambiguity set. We further show that smoothing the dual objective function is equivalent to regularizing the primal objective function, and we identify several ambiguity sets that give rise to several known and new regularization schemes. As a byproduct, we discover an intimate relation between semi-discrete optimal transport problems and discrete choice models traditionally studied in psychology and economics. To solve the regularized optimal transport problems efficiently, we use a stochastic gradient descent algorithm with imprecise stochastic gradient oracles. A new convergence analysis reveals that this algorithm improves the best known convergence guarantee for semi-discrete optimal transport problems with entropic regularizers. [ABSTRACT FROM AUTHOR]

We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vectors. The best known algorithms for this problem run in polynomial time in the maximum of the number of atoms of the two distributions. However, if the components of either random vector are independent, then this number can be exponential in K even though the size of the problem description scales linearly with K. We prove that the described optimal transport problem is #P-hard even if all components of the first random vector are independent uniform Bernoulli random variables, while the second random vector has merely two atoms, and even if only approximate solutions are sought. We also develop a dynamic programming-type algorithm that approximates the Wasserstein distance in pseudo-polynomial time when the components of the first random vector follow arbitrary independent discrete distributions, and we identify special problem instances that can be solved exactly in strongly polynomial time. [ABSTRACT FROM AUTHOR]

A large and growing number of people die with and from dementia, but their care at the end of life is often suboptimal. This article recommends a palliative approach to the care of people with dementia and their families to counter the negative outcomes associated with aggressive medicine and to improve the experience of dying for all concerned. [ABSTRACT FROM AUTHOR]

We consider quadratic stochastic programs with random recourse-a class of problems which is perceived to be computationally demanding. Instead of using mainstream scenario tree-based techniques, we reduce computational complexity by restricting the space of recourse decisions to those linear and quadratic in the observations, thereby obtaining an upper bound on the original problem. To estimate the loss of accuracy of this approach, we further derive a lower bound by dualizing the original problem and solving it in linear and quadratic recourse decisions. By employing robust optimization techniques, we show that both bounding problems may be approximated by tractable conic programs. [ABSTRACT FROM AUTHOR]

We develop tractable semidefinite programming based approximations for distributionally robust individual and joint chance constraints, assuming that only the first- and second-order moments as well as the support of the uncertain parameters are given. It is known that robust chance constraints can be conservatively approximated by Worst-Case Conditional Value-at-Risk (CVaR) constraints. We first prove that this approximation is exact for robust individual chance constraints with concave or (not necessarily concave) quadratic constraint functions, and we demonstrate that the Worst-Case CVaR can be computed efficiently for these classes of constraint functions. Next, we study the Worst-Case CVaR approximation for joint chance constraints. This approximation affords intuitive dual interpretations and is provably tighter than two popular benchmark approximations. The tightness depends on a set of scaling parameters, which can be tuned via a sequential convex optimization algorithm. We show that the approximation becomes essentially exact when the scaling parameters are chosen optimally and that the Worst-Case CVaR can be evaluated efficiently if the scaling parameters are kept constant. We evaluate our joint chance constraint approximation in the context of a dynamic water reservoir control problem and numerically demonstrate its superiority over the two benchmark approximations. [ABSTRACT FROM AUTHOR]

Abstract: The utilization of the cellular metabolism for cofactor regeneration is a common motivation for the application of whole cells in redox biocatalysis. Introduction of an active oxidoreductase into a microorganism has profound consequences on metabolism, potentially affecting metabolic and biotransformation efficiency. An ambitious goal of systems biotechnology is to design process-relevant and knowledge-based engineering strategies to improve biocatalyst performance. Metabolic flux analysis (MFA) has shown that the competition for NAD(P)H between redox biocatalysis and the energy metabolism becomes critical during asymmetric styrene epoxidation catalyzed by growing Escherichia coli containing recombinant styrene monooxygenase. Engineering TCA-cycle regulation allowed increased TCA-cycle activities, a delay of acetate formation, and enhanced NAD(P)H yields during batch cultivation. However, at low biomass and product concentrations, the cellular metabolism of both the mutants as well as the native host strains could cope with increased NADH demands during continuous two-liquid phase biotransformations, whereas elevated but still subtoxic product concentrations were found to cause a significantly increased NAD(P)H demand and a compromised efficiency of metabolic operation. In conclusion, operational conditions determine cellular energy and NAD(P)H demands and thus the biocatalytic efficiency of whole-cell redox biocatalysts. [Copyright &y& Elsevier]

Temporal networks describe workflows of time-consuming tasks whose processing order is constrained by precedence relations. In many cases, the durations of the network tasks can be influenced by the assignment of resources. This leads to the problem of selecting an 'optimal' resource allocation, where optimality is measured by network characteristics such as the makespan (i.e., the time required to complete all tasks). In this paper we study a robust resource allocation problem where the task durations are uncertain, and the goal is to minimise the worst-case makespan. We show that this problem is generically $${\mathcal{NP}}$$ -hard. We then develop convergent bounds on the optimal objective value, as well as feasible allocations whose objective values are bracketed by these bounds. Numerical results provide empirical support for the proposed method. [ABSTRACT FROM AUTHOR]

Selection of the ideal microbe is crucial for whole-cell biotransformations, especially if the target reaction intensively interacts with host cell functions. Asymmetric styrene epoxidation is an example of a reaction which is strongly dependent on the host cell owing to its requirement for efficient cofactor regeneration and stable expression of the styrene monooxygenase genes styAB. On the other hand, styrene epoxidation affects the whole-cell biocatalyst, because it involves toxic substrate and products besides the burden of additional (recombinant) enzyme synthesis. With the aim to compare two fundamentally different strain engineering strategies, asymmetric styrene epoxidation by StyAB was investigated using the engineered wild-type strain Pseudomonas sp. strain VLB120ΔC, a styrene oxide isomerase (StyC) knockout strain able to accumulate ( S)-styrene oxide, and recombinant E. coli JM101 carrying styAB on the plasmid pSPZ10. Their performance was analyzed during fed-batch cultivation in two-liquid phase biotransformations with respect to specific activity, volumetric productivity, product titer, tolerance of toxic substrate and products, by-product formation, and product yield on glucose. Thereby, Pseudomonas sp. strain VLB120ΔC proved its great potential by tolerating high styrene oxide concentrations and by the absence of by-product formation. The E. coli-based catalyst, however, showed higher specific activities and better yields on glucose. The results not only show the importance but also the complexity of host cell selection and engineering. Finding the optimal strain engineering strategy requires profound understanding of bioprocess and biocatalyst operation. In this respect, a possible negative influence of solvent tolerance on yield and activity is discussed. [ABSTRACT FROM AUTHOR]

While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these bespoke loss functions we are able to push Gradient Boosting to match or outperform a wide variety of previously reported classifiers and neural networks. We also investigate the impact of changing the loss function on training time and find that it increases convergence speed up to 8 times faster. As such, these results show that tuning the loss function for Gradient Boosting is a straightforward and computationally efficient method to achieve state-of-the-art performance on imbalanced bioassay datasets without compromising on interpretability and scalability. [ABSTRACT FROM AUTHOR]

We study a robust monopoly pricing problem with a minimax regret objective, where a seller endeavors to sell multiple goods to a single buyer, only knowing that the buyer's values for the goods range over a rectangular uncertainty set. We interpret this pricing problem as a zero-sum game between the seller, who chooses a selling mechanism, and a fictitious adversary or 'nature', who chooses the buyer's values from within the uncertainty set. Using duality techniques rooted in robust optimization, we prove that this game admits a Nash equilibrium in mixed strategies that can be computed in closed form. The Nash strategy of the seller is a randomized posted price mechanism under which the goods are sold separately, while the Nash strategy of nature is a distribution on the uncertainty set under which the buyer's values are comonotonic. We further show that the restriction of the pricing problem to deterministic mechanisms is solved by a deteministic posted price mechanism under which the goods are sold separately. [ABSTRACT FROM AUTHOR]

Continuous linear programs have attracted considerable interest due to their potential for modeling manufacturing, scheduling, and routing problems. While efficient simplex-type algorithms have been developed for separated continuous linear programs, crude time discretization remains the method of choice for solving general (nonseparated) problenl instances. In this paper we propose a more generic approximation scheme for nonseparated continuous linear programs, where we approximate the functional decision variables (policies) by polynomial and piecewise polynomial decision rules. This restriction results in an upper bound on the original problem, which can be computed efficiently by solving a tractable semidefinite program. To estimate the approximation error, we also compute a lower bound by solving a dual continuous lineal program in (piecewise) polynomial decision rules. We establish the convergence of the primal and dual approximations under Stater-type constraint qualifications. We also highlight the potential of our method for optimizing large-scale multiclass queueing systems and dynamic Leontief models. [ABSTRACT FROM AUTHOR]

Next to economic success, ecological considerations have become increasingly important for companies synthesizing various compounds ranging from bulk chemicals to pharmaceuticals. In this context, the economic and ecological feasibility of asymmetric biocatalytic styrene epoxidation has previously been investigated and compared to chemical alternatives. Although the biotechnological two-liquid phase approach was found to be highly interesting in economic terms, the ecological performance is restrained by the applied organic carrier solvent bis(2-ethylhexyl)phthalate, which is toxic to humans and produced from non-renewable resources. As an alternative carrier solvent, the biodiesel constituent ethyl oleate was tested. Furthermore, the switch from glucose to glycerol as a carbon and energy source was investigated, the latter being a cheap abundant resource, as it is a waste product of the biodiesel and soap industries. Both strategies slightly reduced the productivity and final product titer. An ecological and economic assessment on process level, however, revealed a superior environmental performance (by 13%) with ethyl oleate as the extractive solvent, at the expense of slightly reduced economics (by 9%), whereas glycerol use reduced the performance with respect to both aspects. Based on available data, the application of resting cells was evaluated, providing the opportunity of more efficient carbon utilization via decoupling of growth and biotransformation. Their stability is, however, yet to be improved to achieve competitiveness. In general, this study underlines the potential of ecological and economic assessments for systematic process intensification. Even if advantages of proposed changes seem obvious, their true suitability can only be judged by detailed economic and ecological analyses at the process level. [ABSTRACT FROM AUTHOR]

Abstract: We propose a tractable approximation scheme for convex (not necessarily linear) multi-stage robust optimization problems. We approximate the adaptive decisions by finite linear combinations of prescribed basis functions and demonstrate how one can optimize over these decision rules at low computational cost through constraint randomization. We obtain a-priori probabilistic guarantees on the feasibility properties of the optimal decision rule by applying existing constraint sampling techniques to the semi-infinite problem arising from the decision rule approximation. We demonstrate that for a suitable choice of basis functions, the approximation converges as the size of the basis and the number of sampled constraints tend to infinity. The approach yields an algorithm parameterized in the basis size, the probability of constraint violation and the confidence that this probability will not be exceeded. These three parameters serve to tune the trade-off between optimality and feasibility of the decision rules and the computational cost of the algorithm. We assess the convergence and scalability properties of our approach in the context of two inventory management problems. [Copyright &y& Elsevier]

We propose a resource allocation model for project scheduling. Our model accommodates multiple resources and decision-dependent activity durations inspired by microeconomic theory. First, we elaborate a deterministic problem formulation. In a second stage, we enhance this model to account for uncertain problem parameters. Assuming that the first and second moments of these parameters are known, the stochastic model minimises an approximation of the value-at-risk of the project makespan. As a salient feature, our approach employs a scenario-free formulation which is based on normal approximations of the activity path durations. We extend our model to situations in which the moments of the random parameters are ambiguous and describe an iterative solution procedure. Extensive numerical results are provided. [ABSTRACT FROM AUTHOR]

Linear stochastic programming provides a flexible toolbox for analyzing real-life decision situations, but it can become computationally cumbersome when recourse decisions are involved. The latter are usually modeled as decision rules, i.e., functions of the uncertain problem data. It has recently been argued that stochastic programs can quite generally be made tractable by restricting the space of decision rules to those that exhibit a linear data dependence. In this paper, we propose an efficient method to estimate the approximation error introduced by this rather drastic means of complexity reduction: we apply the linear decision rule restriction not only to the primal but also to a dual version of the stochastic program. By employing techniques that are commonly used in modern robust optimization, we show that both arising approximate problems are equivalent to tractable linear or semidefinite programs of moderate sizes. The gap between their optimal values estimates the loss of optimality incurred by the linear decision rule approximation. Our method remains applicable if the stochastic program has random recourse and multiple decision stages. It also extends to cases involving ambiguous probability distributions. [ABSTRACT FROM AUTHOR]

Bio-based processes are commonly accepted to be environmentally preferable to chemical alternatives. Reasons include high selectivities, the avoidance of heavy metals, and mild reaction conditions. However, ecological benefits and economic viability have to be verified in each case. Oxygenases are a very attractive enzyme class, allowing selective oxyfunctionalization by introduction of molecular oxygen into hydrocarbons at optical purities unparalleled by traditional chemical methods. Here, styrene monooxygenase from Pseudomonassp. strain VLB120 was used in recombinant Escherichia colifor the production of enantiopure (S)-styrene oxide from styrene. Substrate and product toxicity was attenuated using a two-liquid phase system with bis(2-ethylhexyl)phthalate as organic carrier solvent. By maintaining previously reported productivities for a longer time period, product concentrations were doubled to 36.3 g Ltot−1making it one of the most productive biocatalytic oxyfunctionalization processes. This biotransformation was incorporated along with an appropriate upstream and downstream processing into a complete process scheme and compared with chemical process alternatives. Ecological assessment showed the bioprocess to be superior to two chemical process alternatives and slightly inferior to the third one, with solvent use being the most critical factor. However, the bioprocess performed best in terms of production costs ($10.2 kg−1). This study underlines the importance of a detailed ecological and economic assessment of bioprocesses to verify their sustainability and to identify weak points with respect to environmental and/or economical sustainability. [ABSTRACT FROM AUTHOR]

In a dynamic investment situation, the right timing of portfolio revisions and adjustments is essential to sustain long-term growth. A high rebalancing frequency reduces the portfolio performance in the presence of transaction costs, whereas a low rebalancing frequency entails a static investment strategy that hardly reacts to changing market conditions. This article studies a family of portfolio problems in a Black-Scholes type economy which depend parametrically on the rebalancing frequency. As an objective criterion we use log-utility, which has strong theoretical appeal and represents a natural choice if the primary goal is long-term performance. We argue that continuous rebalancing only slightly outperforms discrete rebalancing if there are no transaction costs and if the rebalancing intervals are shorter than about one year. Our analysis also reveals that diversification has a dual effect on the mean and variance of the portfolio growth rate as well as on their sensitivities with respect to the rebalancing frequency. [ABSTRACT FROM AUTHOR]

Abstract: Electricity swing options are Bermudan-style path-dependent derivatives on electrical energy. We consider an electricity market driven by several exogenous risk factors and formulate the pricing problem for a class of swing option contracts with energy and power limits as well as ramping constraints. Efficient numerical solution of the arising multistage stochastic program requires aggregation of decision stages, discretization of the probability space, and reparameterization of the decision space. We report on numerical results and discuss analytically tractable limiting cases. [Copyright &y& Elsevier]

Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen negative events. In order to ensure computational tractability, continuous-state stochastic programs are usually discretized; and frequently, the curse of dimensionality dictates that decision stages must be aggregated. In this article we construct two discrete, stage-aggregated stochastic programs which provide upper and lower bounds on the optimal value of the original problem. The approximate problems involve finitely many decisions and constraints, thus principally allowing for numerical solution. [ABSTRACT FROM AUTHOR]

Family caregiving is a large and growing concern in the American workplace. This article describes a pilot program aimed at enhancing the self-efficacy and minimizing the distress of employees who care for relatives with chronic medical conditions. A six-week, community-based education program was adapted to enable these family caregivers to take part in an interactive web-based program. Elements of this innovative program are described along with results of evaluations completed by 49 participants at 3 points in time. This exploratory study found the web-based program to be an effective intervention for employees who are family caregivers and holds promise as an attractive alternative to the traditional classroom approach to caregiver education and support. [ABSTRACT FROM AUTHOR]

In this contribution, the classification of protein binding sites using the physicochemical properties exposed to their pockets is presented. We recently introduced Cavbase, a method for describing and comparing protein binding pockets on the basis of the geometrical and physicochemical properties of their active sites. Here, we present algorithmic and methodological enhancements in the Cavbase property description and in the cavity comparison step. We give examples of the Cavbase similarity analysis detecting pronounced similarities in the binding sites of proteins unrelated in sequence. A similarity search using SARS Mpro protease subpockets as queries retrieved ligands and ligand fragments accommodated in a physicochemical environment similar to that of the query. This allowed the characterization of the protease recognition pockets and the identification of molecular building blocks that can be incorporated into novel antiviral compounds. A cluster analysis procedure for the functional classification of binding pockets was implemented and calibrated using a diverse set of enzyme binding sites. Two relevant protein families, the α-carbonic anhydrases and the protein kinases, are used to demonstrate the scope of our cluster approach. We propose a relevant classification of both protein families, on the basis of the binding motifs in their active sites. The classification provides a new perspective on functional properties across a protein family and is able to highlight features important for potency and selectivity. Furthermore, this information can be used to identify possible cross-reactivities among proteins due to similarities in their binding sites. [Copyright &y& Elsevier]

This article describes the results of an educational intervention for family caregivers of persons in the early stages of Alzheimer's disease. A total of 45 family caregivers participated in this intervention that consisted of five weekly educational sessions about the disease and aspects of caring for someone in the early stages. Measures of knowledge about Alzheimer's disease, self-efficacy, reactions to patients' memory impairment, and level of depression at pretest and posttest were analyzed. Participants' knowledge about the disease improved substantially, self-efficacy and reactions to patients' impairments improved moderately, and level of depression showed no significant improvement. Results suggest that this intervention has merit for family caregivers coping with the early stages of Alzheimer's disease. Implications for implementation and evaluation are discussed. [ABSTRACT FROM AUTHOR]

A new method has been developed to detect functional relationships among proteins independent of a given sequence or fold homology. It is based on the idea that protein function is intimately related to the recognition and subsequent response to the binding of a substrate or an endogenous ligand in a well-characterized binding pocket. Thus, recognition of similar ligands, supposedly linked to similar function, requires conserved recognition features exposed in terms of common physicochemical interaction properties via the functional groups of the residues flanking a particular binding cavity. Following a technique commonly used in the comparison of small molecule ligands, generic pseudocenters coding for possible interaction properties were assigned for a large sample set of cavities extracted from the entire PDB and stored in the database Cavbase. Using a particular query cavity a series of related cavities of decreasing similarity is detected based on a clique detection algorithm. The detected similarity is ranked according to property-based surface patches shared in common by the different clique solutions. The approach either retrieves protein cavities accommodating the same (e.g. co-factors) or closely related ligands or it extracts proteins exhibiting similar function in terms of a related catalytic mechanism. Finally the new method has strong potential to suggest alternative molecular skeletons in de novo design. The retrieval of molecular building blocks accommodated in a particular sub-pocket that shares similarity with the pocket in a protein studied by drug design can inspire the discovery of novel ligands. [Copyright &y& Elsevier]

The objective of this article was to describe the results of an educational intervention for family caregivers of persons in the early stages of Alzheimer's disease. A total of 58 family caregivers participated in this intervention that consisted of five weekly educational sessions about the disease and various aspects of caregiving. Measures of knowledge about Alzheimer's disease, reactions to patients' impairments, and level of depression at pretest, posttest, and follow-up at 9 months posttest were analyzed. The results showed that modest benefits were derived by participants at the three points in time, with knowledge about the disease improving significantly, reactions to patients' impairments improving slightly, and level of depression increasing slightly. This preliminary study reveals that the educational intervention has merit for family caregivers coping with the early stages of Alzheimer's disease, but new or different outcome measures could possibly better assess its impact. [ABSTRACT FROM AUTHOR]

Little attention has been focused on remarriage among older persons and on the effects of chronic illness on late-life marriages and kinship relations. The authors examine the developmental tasks of older stepfamilies and the impact of a dementing illness on family members. Case examples are provided and recommendations offered to enable older stepfamilies to cope with anticipated problems. [ABSTRACT FROM AUTHOR]

The article focuses on Alzheimer's Disease and on how nurses can work around disorientation and aggressive behavior in patients. It states that Alzheimer's disease results in the loss of neurotransmitters that allow brain cells to communicate effectively. It mentions that hospital stays can result in the deterioration of functional and cognitive abilities due to the radical change from home to a busy clinical setting. It comments on the need to ensure the patient has a safe environment.

The goal of scenario reduction is to approximate a given discrete distribution with another discrete distribution that has fewer atoms. We distinguish continuous scenario reduction, where the new atoms may be chosen freely, and discrete scenario reduction, where the new atoms must be chosen from among the existing ones. Using the Wasserstein distance as measure of proximity between distributions, we identify those n-point distributions on the unit ball that are least susceptible to scenario reduction, i.e., that have maximum Wasserstein distance to their closest m-point distributions for some prescribed m < n . We also provide sharp bounds on the added benefit of continuous over discrete scenario reduction. Finally, to our best knowledge, we propose the first polynomial-time constant-factor approximations for both discrete and continuous scenario reduction as well as the first exact exponential-time algorithms for continuous scenario reduction. [ABSTRACT FROM AUTHOR]

Evaluates the effectiveness of home care for the elderly. Role of family members in the delivery of home care; Responsibilities of caregivers in nursing homes; Challenges of giving care to the elderly.

The poem "Rethinking Autonomy" by Daniel Kuhn is presented. First Line: He lived in a shabby hotel off a busy Chicago street. Last Line: Not on our own terms, but taking their cue.

Motivation: Many drug discovery projects fail because the underlying target is finally found to be undruggable. Progress in structure elucidation of proteins now opens up a route to automatic structure-based target assessment. DoGSiteScorer is a newly developed automatic tool combining pocket prediction, characterization and druggability estimation and is now available through a web server.Availability: The DoGSiteScorer web server is freely available for academic use at http://dogsite.zbh.uni-hamburg.deContact: rarey@zbh.uni-hamburg.de. [ABSTRACT FROM AUTHOR]

Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of data points to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual data set, treating the cluster cardinalities as a given input. We cast this problem as a mixed-integer linear program (MILP) that admits tractable semidefinite and linear programming relaxations. We propose deterministic rounding schemes that transform the relaxed solutions to feasible solutions for the MILP. We also prove that these solutions are optimal in the MILP if a cluster separation condition holds. [ABSTRACT FROM AUTHOR]

We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite dimensional LP to tractable finite convex programs in which the performance of the approximation is quantified explicitly. To this end, we adopt the recent developments in two areas of randomized optimization and first-order methods, leading to a priori as well as a posteriori performance guarantees. We illustrate the generality and implications of our theoretical results in the special case of the long-run average cost and discounted cost optimal control problems in the context of Markov decision processes on Borel spaces. The applicability of the theoretical results is demonstrated through a fisheries management problem. [ABSTRACT FROM AUTHOR]

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects. [ABSTRACT FROM AUTHOR]

Some studies have carried out in order to retrieve proteins from the by-product of animal-processing industries. Earthworms are rich in protein and usually are used in animal feed. Thus, this study aimed to optimize the hydrolysis process ofEisenia andreiearthworms by employing Alcalase enzyme. Using the response surface methodology, we evaluated the following conditions: temperature, hydrolysis time, stirring speed, and enzyme/substrate ratio. The optimal conditions for the experimental design were determined through the analysis of the foaming and emulsifying properties,in vitrostarch digestibility, and antioxidant activity. The results demonstrate that the highest degree of hydrolysis (i.e., 92%) was obtained under the following conditions: pH, 9.5; temperature, 25 °C; hydrolysis time, 2.25 h; stirring speed, 200 rpm; and enzyme/substrate ratio, 1.77%, using Alcalase enzyme. Evaluation of the amino acid composition under these conditions revealed higher concentrations of aspartic acid, glutamic acid, and leucine. Thein vitroprotein digestibility of the hydrolysate was approximately 73%. There were no significant improvements in either foam stability or emulsification after enzymatic hydrolysis. Additional studies on the antioxidant activity are required. This bioproduct could potentially serve as a promising supplementary food product. [ABSTRACT FROM AUTHOR]

The growing preoccupation about drinking water quality available on earth, a new order of emergent pollutants in rivers and lakes has called the attention of the scientific community, the micropollutants. These compounds come from the irregular or involuntary ejectment of substances such as pesticides, drugs, insecticides and other into the nature. By having a complex structure, the micropollutants have high resistance over the traditional water treatment to human consume. Thus, alternatives methodologies to their degradation have been studied, such as advanced oxidation process (AOPs). The AOPs are based in the formation of hydroxyls radicals (OH-) highly reactive that degrade complexes substances into lesser aggressive products as CO2 and water. Even though many methods to generate hydroxyls radicals from advanced oxidation processes exist, they differentiate on the application and viability. This review shows different methodologies using AOPs and their respective application characteristics, also some works already made in complexes substances treatment, which prove their efficiency. [ABSTRACT FROM AUTHOR]

This study aimed to determine the effects of different feeding strategies in fed-batch cultivations on the production of β-galactosidase fused to a cellulose-binding domain (CBD) by Escherichia coli C41(DE3) using a bench bioreactor. The highest enzyme activities (∼17,000 U/L) were obtained in fed-batch cultivations controlled by DO-stat and induced with lactose (5 g/L) at 18 h of cultivation. Under these cultivation conditions, the maximum values of β-galactosidase yields per substrate, β-galactosidase yields per biomass, and Q P were approximately 950 U/g glucose , 1100 U/g cells , and 540 U/L.h, respectively, using Terrific Broth as culture medium and concentrated feed solution (2 ×). The dairy coproducts were efficient in inducing the expression of the recombinant enzyme, having reached the highest values of β-galactosidase-CBD activity after 40 h of cultivation. The maximum enzyme activities obtained when using whey permeate or ricotta whey as inducers were 22,950 or 24,640 U/L, respectively. Using the optimal temperature of E. coli growth before induction and ricotta whey as an inducer increased enzyme productivity in 50%. Plasmid construction showed high stability (>94%) throughout E. coli cultivation. This study demonstrated the feasibility of using the DO-stat feeding strategy to produce β-galactosidase-CBD employing ricotta whey as an inducer of enzyme expression. [Display omitted] • β-Galactosidase was produced at a benchtop bioreactor in fed-batch cultivations. • Lactose was used to induce the recombinant β-galactosidase expression. • DO-stat feeding strategy resulted in the highest kinetic parameters of cultivations. • The higher β-galactosidase activity was reached using ricotta whey as inducer. [ABSTRACT FROM AUTHOR]