A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment.

Minimizing a company's operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of uncertainty or risk involved. In this study, we focus on a processing production plant as a model for a chain of operations and supply chain actions. We consider the stochastically varying production and transportation costs from the site to the plant, as well as from the plant to the customer base. Through stochastic optimization, we demonstrate that the plant producer can benefit from improved financial outcomes by setting higher sale prices while simultaneously lowering optimized production costs. This can be accomplished by selectively choosing producers whose production cost probability density function follows a Pareto distribution. Notably, a lower Pareto exponent yields better supply chain cost optimization predictions. Alternatively, a Gaussian stochastic fluctuation may be proposed as a more suitable choice when trading off optimization and simplicity. Although this may result in slightly less optimal performance, it offers advantages in terms of ease of implementation and computational efficiency.

Natural history of dental caries: Baseline characteristics of the VicGen birth cohort study.

BACKGROUND: Early-life dental caries is a major global health problem. Children's first dental visit is recommended at 2 years age. The VicGeneration (VicGen) oral health birth cohort study aims to understand the multifactorial nature of early childhood caries. This report describes the baseline characteristics of children in the VicGen study. METHODS: We merged data between the first (at birth) and fourth waves (18 month age) to assess dental caries among children (primary outcome) and other oral diseases (secondary outcomes) employing t tests, chi-square tests, Fisher's exact tests, and Cochran-Mantel-Haenszel tests using IBM-SPSS(v25). RESULTS: Most children lived in metros with two-parent families. Most guardians were women graduated from high school. Twenty-seven of 389 (6.94%) 18-month-old children experienced dental caries. More children living in rural areas (vs. urban) experienced caries. Females were more likely to experience caries (OR: 2.16). Several children had other oral health problems. In early life, children's oral examination was conducted by midwives, breastfeeding/lactation consultants, hospital nurses, speech pathologists, and breastfeeding clinic staff. CONCLUSION: VicGen baseline characteristics show that almost 7% of the 18-month-old children experienced caries. There is a need to advance children's recommended first dental visit date and to train early-life healthcare professionals about oral diseases.

Serum selenium and pancreatic cancer: a prospective study in the Prostate, Lung, Colorectal and Ovarian Cancer Trial cohort.

PURPOSE: Pancreatic cancer(PCa) is one of the most lethal cancers with few known consistent nutrition-related risk factors. Epidemiologic associations between the trace element selenium and PCa are inconsistent. This study examined the association of pre-diagnostic serum selenium with incident PCa. METHODS: We conducted a nested case-control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Study (PLCO) cohort of men and women 55-70 years old at baseline (1993-2001). In total, 303 PCa cases developed during the 17-year follow-up period (1993-2009). We selected two controls (n = 606) for each case who were alive at the time the case was diagnosed who were matched on age, sex, race, and date of blood draw. We used conditional logistic regression analysis to calculate the odds ratio (OR) and 95% confidence intervals (CI) adjusting for smoking status and diabetes mellitus. RESULTS: Mean serum selenium concentrations were slightly lower in cases (mean, 95% CI: 139.0 ng/ml, 135.6-138.9) compared to controls (142.5 ng/ml, 140.4-142.4, p = 0.08). Overall, serum selenium was not associated with PCa risk (continuous OR: 0.66; 0.32-1.37). There was no significant interaction by sex, smoking, diabetes, or follow-up time (p > 0.05). CONCLUSION: Our results do not support the hypothesis that serum selenium is associated with PCa risk.

Recent Progress in the Chemistry of Daphniphyllum Alkaloids †.

Daphniphyllum is an evergreen species known since 1826. After initial systematic investigations, more than 320 members of this family have been isolated, which comprise complex and fascinating structures. Unique azapolycyclic architectures containing one or more quaternary stereocenters render these alkaloids synthetically challenging. This review covers efforts toward the synthesis of Daphniphyllum alkaloids spanning the period from 2005 to the beginning of 2016, including reported biological activities as well as the isolation of new members of this genus.

Statistics of Correlations and Fluctuations in a Stochastic Model of Wealth Exchange.

In our recently proposed stochastic version of discretized kinetic theory, the exchange of wealth in a society is modelled through a large system of Langevin equations. The deterministic part of the equations is based on non-linear transition probabilities between income classes. The noise terms can be additive, multiplicative or mixed, both with white or Ornstein-Uhlenbeck spectrum. The most important measured correlations are those between Gini inequality index G and social mobility M, between total income and G, and between M and total income. We describe numerical results concerning these correlations and a quantity which gives average stochastic deviations from the equilibrium solutions in dependence on the noise amplitude.

Protein lipograms.

Linguistic analysis of protein sequences is an underexploited technique. Here, we capitalize on the concept of the lipogram to characterize sequences at the proteome levels. A lipogram is a literary composition which omits one or more letters. A protein lipogram likewise omits one or more types of amino acid. In this article, we establish a usable terminology for the decomposition of a sequence collection in terms of the lipogram. Next, we characterize Uniref50 using a lipogram decomposition. At the global level, protein lipograms exhibit power-law properties. A clear correlation with metabolic cost is seen. Finally, we use the lipogram construction to assign proteomes to the four branches of the tree-of-life: archaea, bacteria, eukaryotes and viruses. We conclude from this pilot study that the lipogram demonstrates considerable potential as an additional tool for sequence analysis and proteome classification.

A statistical physics perspective on alignment-independent protein sequence comparison.

MOTIVATION: Within bioinformatics, the textual alignment of amino acid sequences has long dominated the determination of similarity between proteins, with all that implies for shared structure, function and evolutionary descent. Despite the relative success of modern-day sequence alignment algorithms, so-called alignment-free approaches offer a complementary means of determining and expressing similarity, with potential benefits in certain key applications, such as regression analysis of protein structure-function studies, where alignment-base similarity has performed poorly. RESULTS: Here, we offer a fresh, statistical physics-based perspective focusing on the question of alignment-free comparison, in the process adapting results from 'first passage probability distribution' to summarize statistics of ensemble averaged amino acid propensity values. In this article, we introduce and elaborate this approach.

Strategies toward the Total Synthesis of Calyciphylline B-type Alkaloids: A Computational Perspective Aided by DFT Analysis.

Herein we describe synthetic efforts toward the total synthesis of calyciphylline B-type alkaloids. In the process, we disclose a detailed DFT study of equilibrium geometries and transition states that explains the stereochemical outcome during the formation of critical intermediates. X-ray crystallographic analysis reveals interesting conformational features in the naturally occurring deoxycalyciphylline B and its synthetic congeners.

Synthesis of a Model Tetracyclic Core Structure of Calyciphylline B-Type Alkaloids.

Herein, we report the enantioselective synthesis of a functionalized aza-octahydropentalene and its elaboration to a model tetracyclic core structure of calyciphylline B-type alkaloids.

Total Synthesis of Isodaphlongamine†H: A Possible Biogenetic Conundrum.

Herein we describe the first synthetic efforts toward the total synthesis of isodaphlongamine H, a calyciphylline B-type alkaloid. The strategy employs a chemoenzymatic process for the preparation of a functionalized cyclopentanol with a quaternary center. This molecule is elaborated to form an enantiopure 1-aza-perhydrocyclopentalene core, representing rings A and E of all calyciphylline B-type alkaloids. Further transformations involve the formation of a cyclic enaminone, 1,4-conjugate addition with a cyclopentenyl subunit, and intramolecular aldol cyclization to achieve a pentacyclic intermediate, ultimately forming isodaphlongamine H in a total of 24 steps from the commercially available compound 2-carbethoxycyclopentanone. Isodaphlongamine H exhibits promising inhibitory activity against a panel of human cancer cell lines.

Disparities in unmet dental need and dental care received by pregnant women in Maryland.

To examine prenatal dental care needs, utilization and oral health counseling among Maryland women who delivered a live infant during 2001-2003 and identify the factors associated with having a dental visit and having an unmet dental need during pregnancy. Pregnancy Risk Assessment Monitoring System is an ongoing population based surveillance system that collects information of women's attitudes and experiences before, during, and shortly after pregnancy. Logistic regression was used to model dental visits and unmet dental need using predictor variables for Maryland 2001-2003 births. Less than half of all women reported having a dental visit and receiving oral health advice during pregnancy. Twenty-five percent of women reported a need for dental care, of which 33 % did not receive dental care despite their perceived need. Multivariate modeling revealed that racial minorities, women who were not married and those with annual income <$40,000 were least likely to have a dental visit. Women who were not married, had low annual income, were older than 40 years of age, had an unintended pregnancy and received prenatal care later than desired were most likely to have an unmet dental need during pregnancy. Despite reported needs and existing recommendations to include oral health as a component of prenatal care, less than half of pregnant women have a dental visit during their pregnancy. One-third of women with a dental problem did not have a dental visit highlighting the unmet need for dental care during pregnancy.

Effect of vaccine dose intervals: Considering immunity levels, vaccine efficacy, and strain variants for disease control strategy.

In this study, we present an immuno-epidemic model to understand mitigation options during an epidemic break. The model incorporates comorbidity and multiple-vaccine doses through a system of coupled integro-differential equations to analyze the epidemic rate and intensity from a knowledge of the basic reproduction number and time-distributed rate functions. Our modeling results show that the interval between vaccine doses is a key control parameter that can be tuned to significantly influence disease spread. We show that multiple doses induce a hysteresis effect in immunity levels that offers a better mitigation alternative compared to frequent vaccination which is less cost-effective while being more intrusive. Optimal dosing intervals, emphasizing the cost-effectiveness of each vaccination effort, and determined by various factors such as the level of immunity and efficacy of vaccines against different strains, appear to be crucial in disease management. The model is sufficiently generic that can be extended to accommodate specific disease forms.

Discovery and Optimization of Aryl Piperidinone Ureas as Selective Formyl Peptide Receptor 2 Agonists.

We report the discovery and optimization of aryl piperidinone urea formyl peptide receptor 2 (FPR2) agonists from a weakly active high-throughput screening (HTS) hit to potent and selective agonists with favorable efficacy in acute in vivo models. A basis for the selectivity for FPR2 over FPR1 is proposed based on docking molecules into recently reported FPR2 and FPR1 cryoEM structures. Compounds from the new scaffold reported in this study exhibited superior potency and selectivity and favorable ADME profiles. Furthermore, select compounds were evaluated in an acute rat lipopolysaccharide (LPS) inflammation model and demonstrated robust dose-dependent induction of IL10, a marker for inflammation resolution, providing a valuable proof of concept for this class of FPR2 agonists.

Studies toward the generation of functionalized quaternary carbon centers relying on Wittig and Wittig-Still allylic ether anionic transpositions.

Although the [2,3]-Wittig and Wittig-Still rearrangements have long been known, their application in the generation of quaternary carbon centers in carbocyclic ring systems is sparse. Model studies utilizing this strategy and possible mechanisms are discussed herein. Unprecedented examples of an α-elimination pathway from stannylmethyl allyl ethers as a major undesired product in some Wittig-Still rearrangements are reported.

Probing functional diversity in pactamycin toward antibiotic, antitumor, and antiprotozoal activity.

A total of eight new analogs of pactamycin were prepared and tested alongside pactamycin and three of its natural congeners for antibacterial, anticancer, and antiprotozoal activities. The present study highlights the effects of changing the urea and aniline groups especially with regard to anticancer and antiprotozoal activities.

Smoking as a risk factor for oral candidiasis in HIV-infected adults.

OBJECTIVE: We aimed to examine if smoking is an independent predictor of oral candidiasis (OC) among HIV-1 infected persons. METHODS: The cross-sectional part of this study evaluated 631 adult dentate HIV-1 seropositive persons examined for OC from 1995-2000 at the University of North Carolina Hospitals in Chapel Hill, NC. In the second part, from the above sample, a total of 283 individuals who were free of HIV-associated oral diseases at baseline were followed up for 2 years to assess incident OC events. Data collected from medical record review, interview questionnaires, and clinical examinations were analyzed using chi-squared tests and t-tests. Logistic regression models were developed for prevalent OC employing the likelihood ratio test, whereas Poisson regression models were developed for assessing cumulative incidence of OC. These models included a variety of independent variables to adjust for confounding. RESULTS: Thirteen percent of participants had OC only; 4.6% had OC with Oral Hairy Leukoplakia; and 69.7% had neither. Smoking was associated with OC in all models [prevalent OC - current smokers: logistic regression - Odd ratio (95% CI) = 2.5 (1.3, 4.8); Incident OC - current smokers: Poisson regression (main effects model) - Incidence rate ratio (95% CI) = 1.9 (1.1, 3.8)]. Other Poisson regression models suggested evidence for effect modification between CD4 cell count and incident OC by smoking. CONCLUSION: Smoking is an independent risk factor for the development of OC in HIV-1 infected persons, and the risk of OC is modified by CD4 cell count which measures strength of the immune system.

A Topological Distance Between Multi-Fields Based on Multi-Dimensional Persistence Diagrams.

The problem of computing topological distance between two scalar fields based on Reeb graphs or contour trees has been studied and applied successfully to various problems in topological shape matching, data analysis, and visualization. However, generalizing such results for computing distance measures between two multi-fields based on their Reeb spaces is still in its infancy. Towards this, in the current paper we propose a technique to compute an effective distance measure between two multi-fields by computing a novel multi-dimensional persistence diagram (MDPD) corresponding to each of the (quantized) Reeb spaces. First, we construct a multi-dimensional Reeb graph (MDRG), which is a hierarchical decomposition of the Reeb space into a collection of Reeb graphs. The MDPD corresponding to each MDRG is then computed based on the persistence diagrams of the component Reeb graphs of the MDRG. Our distance measure extends the Wasserstein distance between two persistence diagrams of Reeb graphs to MDPDs of MDRGs. We prove that the proposed measure is a pseudo-metric and satisfies a stability property. Effectiveness of the proposed distance measure has been demonstrated in (i) shape retrieval contest data - SHREC 2010 and (ii) Pt-CO bond detection data from computational chemistry. Experimental results show that the proposed distance measure based on the Reeb spaces has more discriminating power in clustering the shapes and detecting the formation of a stable Pt-CO bond as compared to the similar measures between Reeb graphs.

Extracting prime protein targets as possible drug candidates: machine learning evaluation.

Extracting "high ranking" or "prime protein targets" (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations - ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository ( http://DUD-E.decoys.org ) are matched against 1040 ligands using "forward modeling" machine learning for initial "data mining and modeling" (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called "reverse modeling" (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening.

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

A Topological Similarity Measure Between Multi-Resolution Reeb Spaces.

Searching similarity between a pair of shapes or data is an important problem in data analysis and visualization. The problem of computing similarity measures using scalar topology has been studied extensively and proven useful in the shape and data matching. Even though multi-field or multivariate (consists of multiple scalar fields) topology reveals richer topological features, research on building tools for computing similarity measures using multi-field topology is still in its infancy. In the current article, we propose a novel similarity measure between two piecewise-linear multi-fields based on their multi-resolution Reeb spaces - a newly developed data-structure that captures the topology of a multi-field. Overall, our method consists of two steps: (i) building a multi-resolution Reeb space corresponding to each of the multi-fields and (ii) proposing a similarity measure between two multi-resolution Reeb spaces by computing a list of topologically consistent matching pairs (of nodes) and the similarity between them. We demonstrate the effectiveness of the proposed similarity measure in detecting topological features from real time-varying multi-field data in two application domains - one from computational physics and one from computational chemistry.