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.

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.

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 model of conflict and leadership: Is there a hawkish drift in politics?

We analyze conflict between a citizenry and an insurgent group over a fixed resource such as land. The citizenry has an elected leader who proposes a division such that, the lower the land ceded to the insurgents, the higher the cost of conflict. Leaders differ in ability and ideology such that the higher the leader's ability, the lower the cost of conflict, and the more hawkish the leader, the higher his utility from retaining land. We show that the conflict arises from the political process with re-election motives causing leaders to choose to cede too little land to signal their ability. We also show that when the rents of office are high, the political equilibrium and the second best diverge; in particular, the policy under the political equilibrium is more hawkish compared to the second best. When both ideology and ability are unknown, we provide a plausible condition under which the probability of re-election increases in the leader's hawkishness, thereby providing an explanation for why hawkish politicians may have a natural advantage under the electoral process.

Development of Plant-Based Larvicide and Herbal Mosquito Repellent Fast Card with Reference to Identification of the Functional Bioactive Compounds Effective Against Culex Mosquito.

Warm and humid climate creates ideal conditions for mosquito breeding. The ability of these vectors to spread a number of diseases to humans causes millions of deaths every year. Indiscriminate use of synthetic insecticides leads to the development of resistance in vector mosquitoes and along with this, these pesticides cause biological magnification of toxic components and affects adversely the non-target organisms including human being. Commercially available, chemically manufactured mosquito repellent fast cards are convenient to use and quite effective but burning of such card generates a lot of smoke and might be hazardous to human health in the long run. Thus, alternative approaches are to be adopted to control the population load of vector mosquito. Like that, the present study also reveals the larvicidal effect of Duranta leaf extract against Culex mosquito. In the present study, mosquito repellent fast card has been developed by Duranta-algal mixture which has shown better result than commercially available fast card on the basis of mosquito mortality as well as the amount of gases emitted. Again, the ethanolic crude extract of Duranta leaves leads to 100% mortality of all instars (Culex pipiens) larvae at both 1000 ppm and 500 ppm concentration. Therefore, the active component of Duranta has also been investigated. In Duranta, highest area percentage and peak have been shown by propionic acid in the retention time 18.086 by GC-MS. So, it can be confirmed that the major active ingredient is propionic acid in Duranta which is responsible for the mosquitocidal properties. Occurrence of propionic acid in Duranta has also been confirmed by the HPLC analysis.

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.

Oral health surveillance in Australia: the need for ongoing data to inform public health decision-making.

Surveillance of people's health takes on an important meaning in the practice of public health because it allows monitoring of diseases and prompt response to change in proportions and rates at which diseases occur in populations. Improving health of populations requires establishment of an effective public health system. Population level data and analysis is critically important in government policy and program development and monitoring. Lack of or inadequate information about the health of populations leads to ineffective policies that may often attenuate health problems instead of solving them. Australia's current oral health surveillance is mostly through ad hoc sentinel surveys, which lack recency in time. This position paper is to present the need for real-time oral health surveillance in Australia, which can be used to inform health decision-making in a timely manner.

Infection kinetics of Covid-19 and containment strategy.

The devastating trail of Covid-19 is characterized by one of the highest mortality-to-infected ratio for a pandemic. Restricted therapeutic and early-stage vaccination still renders social exclusion through lockdown as the key containment mode.To understand the dynamics, we propose PHIRVD, a mechanistic infection propagation model that Machine Learns (Bayesian Markov Chain Monte Carlo) the evolution of six infection stages, namely healthy susceptible (H), predisposed comorbid susceptible (P), infected (I), recovered (R), herd immunized (V) and mortality (D), providing a highly reliable mortality prediction profile for 18 countries at varying stages of lockdown. Training data between 10 February to 29 June 2020, PHIRVD can accurately predict mortality profile up to November 2020, including the second wave kinetics. The model also suggests mortality-to-infection ratio as a more dynamic pandemic descriptor, substituting reproduction number. PHIRVD establishes the importance of early and prolonged but strategic lockdown to contain future relapse, complementing futuristic vaccine impact.

A social engineering model for poverty alleviation.

Poverty, the quintessential denominator of a developing nation, has been traditionally defined against an arbitrary poverty line; individuals (or countries) below this line are deemed poor and those above it, not so! This has two pitfalls. First, absolute reliance on a single poverty line, based on basic food consumption, and not on total consumption distribution, is only a partial poverty index at best. Second, a single expense descriptor is an exogenous quantity that does not evolve from income-expenditure statistics. Using extensive income-expenditure statistics from India, here we show how a self-consistent endogenous poverty line can be derived from an agent-based stochastic model of market exchange, combining all expenditure modes (basic food, other food and non-food), whose parameters are probabilistically estimated using advanced Machine Learning tools. Our mathematical study establishes a consumption based poverty measure that combines labor, commodity, and asset market outcomes, delivering an excellent tool for economic policy formulation.

CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway.

Studies on the influence of a modern lifestyle in abetting Coronary Heart Diseases (CHD) have mostly focused on deterrent health factors, like smoking, alcohol intake, cheese consumption and average systolic blood pressure, largely disregarding the impact of a healthy lifestyle in mitigating CHD risk. In this study, 30+ years' World Health Organization (WHO) data have been analyzed, using a wide array of advanced Machine Learning techniques, to quantify how regulated reliance on positive health indicators, e.g. fruits/vegetables, cereals can offset CHD risk factors over a period of time. Our research ranks the impact of the negative outliers on CHD and then quantifies the impact of the positive health factors in mitigating the negative risk-factors. Our research outcomes, presented through simple mathematical equations, outline the best CHD prevention strategy using lifestyle control only. We show that a 20% increase in the intake of fruit/vegetable leads to 3-6% decrease in SBP; or, a 10% increase in cereal intake lowers SBP by 3%; a simultaneous increase of 10% in fruit-vegetable can further offset the effects of SBP by 6%. Our analysis establishes gender independence of lifestyle on CHD, refuting long held assumptions and unqualified beliefs. We show that CHD risk can be lowered with incremental changes in lifestyle and diet, e.g. fruit-vegetable intake ameliorating effects of alcohol-smoking-fatty food. Our multivariate data model also estimates functional relationships amongst lifestyle factors that can potentially redefine the diagnostics of Framingham score-based CHD-prediction.

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.

An epidemiological study of dental caries and associated factors among children residing in orphanages in Kerala, India: Health in Orphanages Project (HOPe).

BACKGROUND: It is estimated that, as of 2010, there were 32 million orphaned children in India. There is little published information on the oral health of children in orphanages in India. AIM: To determine caries status and associated risk factors among children in orphanages in Kerala, India. METHODS: This cross-sectional study assessed caries using World Health Organization (WHO) criteria, and caries experience was reported as decayed, missing and filled primary or secondary teeth (dmft or DMFT, respectively). A brief questionnaire captured information on child oral health behaviours. Mean [standard deviation (SD)] and median [interquartile range (IQR)] scores were used to describe caries rates. Multivariable logistic regression analysis was conducted to identify independent disease predictors. Study design complexities, such as clustering by orphanage and stratification by district, were accounted for in the multivariable regression analysis. This was carried out using the survey commands in STATA 13. A value of P<0.05 was considered as statistically significant. RESULTS: Overall, 1,137 children residing in 31 orphanages across the State of Kerala were recruited to the study. Female children made up 82% of the sample. In 6-year-old children the prevalence of caries was 77% and the mean dmft score was 3.60 (SD= 3.50); in 12-year-old children the prevalence of caries was 44% and the mean DMFT score was 1.35 (SD = 1.96). Among 12-year-old children, those who reported being shown how to clean their teeth were less likely to have caries (odds ratio = 0.62; 95% confidence interval: 0.38-0.95). CONCLUSION: Caries rates among children in orphanages were much higher than among children in the general population in Kerala. There is an urgent need for evidence-based and sustainable primary prevention strategies to reduce the burden of caries in this highly vulnerable population.

Role of external and internal perturbations on ferromagnetic phase transitions in manganites: existence of tricritical points.

A phenomenological mean-field theory is presented to describe the role of external magnetic field, pressure and chemical substitution on the nature of ferromagnetic (FM) to paramagnetic (PM) phase transition in manganites. The application of external field (or pressure) shifts the transition, leading to a field (or pressure) dependent phase boundary along which a tricritical point is shown to exist where a first-order FM-PM transition becomes second-order. We show that the effect of chemical substitution on the FM transition is analogous to that of external perturbations (magnetic field and pressure); this includes the existence of a tricritical point at which the order of transition changes. Our theoretical predictions satisfactorily explain the nature of FM-PM transition, observed in several systems. The modeling hypothesis has been critically verified from our experimental data from a wide range of colossal magnetoresistive manganite single crystals like Sm0.52Sr0.48MnO3. The theoretical model prediction of a tricritical point has been validated in this experiment which provides a major ramification of the strength of the model proposed.

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.

Double diffusivity model under stochastic forcing.

The "double diffusivity" model was proposed in the late 1970s, and reworked in the early 1980s, as a continuum counterpart to existing discrete models of diffusion corresponding to high diffusivity paths, such as grain boundaries and dislocation lines. It was later rejuvenated in the 1990s to interpret experimental results on diffusion in polycrystalline and nanocrystalline specimens where grain boundaries and triple grain boundary junctions act as high diffusivity paths. Technically, the model pans out as a system of coupled Fick-type diffusion equations to represent "regular" and "high" diffusivity paths with "source terms" accounting for the mass exchange between the two paths. The model remit was extended by analogy to describe flow in porous media with double porosity, as well as to model heat conduction in media with two nonequilibrium local temperature baths, e.g., ion and electron baths. Uncoupling of the two partial differential equations leads to a higher-ordered diffusion equation, solutions of which could be obtained in terms of classical diffusion equation solutions. Similar equations could also be derived within an "internal length" gradient (ILG) mechanics formulation applied to diffusion problems, i.e., by introducing nonlocal effects, together with inertia and viscosity, in a mechanics based formulation of diffusion theory. While being remarkably successful in studies related to various aspects of transport in inhomogeneous media with deterministic microstructures and nanostructures, its implications in the presence of stochasticity have not yet been considered. This issue becomes particularly important in the case of diffusion in nanopolycrystals whose deterministic ILG-based theoretical calculations predict a relaxation time that is only about one-tenth of the actual experimentally verified time scale. This article provides the "missing link" in this estimation by adding a vital element in the ILG structure, that of stochasticity, that takes into account all boundary layer fluctuations. Our stochastic-ILG diffusion calculation confirms rapprochement between theory and experiment, thereby benchmarking a new generation of gradient-based continuum models that conform closer to real-life fluctuating environments.

Poverty index with time-varying consumption and income distributions.

Starting from a stochastic agent-based model to represent market exchange in a developing economy, we study time variations of the probability density function of income with simultaneous variation of the consumption deprivation (CD), where CD represents the shortfall in consumption from the saturation level of an essential commodity, cereal. Together, these two models combine income-expenditure-based market dynamics with time variations in consumption due to income. In this new unified theoretical structure, exchange of trade in assets is only allowed when the income exceeds consumption-deprivation while CD itself is endogenously obtained from a separate kinetic model. Our results reveal that the nature of time variation of the CD function leads to a downward trend in the threshold level of consumption of basic necessities, suggesting a possible dietary transition in terms of lower saturation level of food-grain consumption, possibly through an improvement in the level of living. The new poverty index, defined as CD, is amenable to approximate probabilistic prediction within a short time horizon. A major achievement of this work is the intrinsic independence of the poverty index from an exogenous poverty line, making it more objective for policy formulation as opposed to existing poverty indices in the literature.

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.