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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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ghosh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors declare no competing financial interest., (© 2024 American Chemical Society.)

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., (© 2023. The Author(s).)

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.

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., (© 2022. The Author(s).)

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier Ltd. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist.

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.

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.

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.

Dengue is one of the deadliest mosquito-borne disease prevalent mainly in tropical and sub-tropical regions. Controlling the spread of this disease becomes a major concern to the public health authority. World Health Organization (WHO) adopted several mosquito control strategies to reduce the disease prevalence. In this work, a general multi-patch non-autonomous dengue model is formulated to capture the temporal and spatial transmission mechanism of the disease and the effectiveness of different adult mosquito control strategies in reducing dengue prevalence is evaluated. During the period (2014-2015) the dengue situation of Kolkata which is one of the most dengue affected city in India is considered in our study. Depending on geographical location, Kolkata is divided into five regions and our model is fitted to the monthly dengue cases of these five regions during the above-mentioned period. By considering control specific characteristics (e.g. efficacy, environment persistence) of the mosquito control strategies, we study the efficiency of three adult mosquito controls and their combined effect in reducing dengue prevalence. From our study, it is observed that control with higher environment persistence performs better in comparison to the controls having low environment persistence. It is also observed that, connectedness between the regions play a key role in the effectiveness of the control strategies., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

This paper proposes k -medoids clustering method to reveal the distinct groups of 1318 variable stars in the Galaxy based on their light curves, where each light curve represents the graph of brightness of the star against time. To overcome the deficiencies of subjective traditional classification, we separate the stars more scientifically according to their geometrical configuration and show that our approach outperforms the existing classification schemes in astronomy. It results in two optimum groups of eclipsing binaries corresponding to bright, massive systems and fainter, less massive systems., Competing Interests: No potential conflict of interest was reported by the authors., (© 2019 Informa UK Limited, trading as Taylor & Francis Group.)

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 Sm 0.52 Sr 0.48 MnO 3 . 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.

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., Competing Interests: The authors declare no conflict of interest.

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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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.

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.

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.

The dynamical evolution of dislocations in plastically deformed metals is controlled by both deterministic factors arising out of applied loads and stochastic effects appearing due to fluctuations of internal stress. Such types of stochastic dislocation processes and the associated spatially inhomogeneous modes lead to randomness in the observed deformation structure. Previous studies have analyzed the role of randomness in such textural evolution, but none of these models have considered the impact of a finite decay time (all previous models assumed instantaneous relaxation which is "unphysical") of the stochastic perturbations in the overall dynamics of the system. The present article bridges this knowledge gap by introducing a colored noise in the form of an Ornstein-Uhlenbeck noise in the analysis of a class of linear and nonlinear Wiener and Ornstein-Uhlenbeck processes that these structural dislocation dynamics could be mapped on to. Based on an analysis of the relevant Fokker-Planck model, our results show that linear Wiener processes remain unaffected by the second time scale in the problem, but all nonlinear processes, both the Wiener type and Ornstein-Uhlenbeck type, scale as a function of the noise decay time τ. The results are expected to ramify existing experimental observations and inspire new numerical and laboratory tests to gain further insight into the competition between deterministic and random effects in modeling plastically deformed samples.

Loss of coherence with increasing excitation amplitudes and spatial size modulation is a fundamental problem in designing Raman fiber lasers. While it is known that ramping up laser pump power increases the amplitude of stochastic excitations, such higher energy inputs can also lead to a transition from a linearly stable coherent laminar regime to a non-desirable disordered turbulent state. This report presents a new statistical methodology, based on first passage statistics, that classifies lasing regimes in Raman fiber lasers, thereby leading to a fast and highly accurate identification of a strong instability leading to a laminar-turbulent phase transition through a self-consistently defined order parameter. The results have been consistent across a wide range of pump power values, heralding a breakthrough in the non-invasive analysis of fiber laser dynamics.

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.

In oscillatory reaction-diffusion systems, time-delay feedback can lead to the instability of uniform oscillations with respect to formation of standing waves. Here, we investigate how the presence of additive, Gaussian white noise can induce the appearance of standing waves. Combining analytical solutions of the model with spatiotemporal simulations, we find that noise can promote standing waves in regimes where the deterministic uniform oscillatory modes are stabilized. As the deterministic phase boundary is approached, the spatiotemporal correlations become stronger, such that even small noise can induce standing waves in this parameter regime. With larger noise strengths, standing waves could be induced at finite distances from the (deterministic) phase boundary. The overall dynamics is defined through the interplay of noisy forcing with the inherent reaction-diffusion dynamics.

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.

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., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Among many other strategies, the enaminone approach is an important strategy to construct and diversify the azacyclic core in various alkaloids syntheses. In this brief review we discuss the application of cyclic enaminones as building blocks, as well as potential intermediates in the total synthesis of selected alkaloids.

Cyclic enaminones are versatile intermediates to construct a variety of azacyclic frameworks and have been widely used in alkaloid synthesis. Here, we summarize three approaches for stereoselective syntheses of cyclic enaminones and their functionalized derivatives. These include chiral substrates (chirons) as starting materials, syntheses employing non-catalytic (stoichiometric) reagents, and catalytic asymmetric methods.

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., (© The Author 2015. Published by Oxford University Press.)

The article analyzes the contribution of stochastic thermal fluctuations in the attachment times of the immature T-cell receptor TCR: peptide-major-histocompatibility-complex pMHC immunological synapse bond. The key question addressed here is the following: how does a synapse bond remain stabilized in the presence of high-frequency thermal noise that potentially equates to a strong detaching force? Focusing on the average time persistence of an immature synapse, we show that the high-frequency nodes accompanying large fluctuations are counterbalanced by low-frequency nodes that evolve over longer time periods, eventually leading to signaling of the immunological synapse bond primarily decided by nodes of the latter type. Our analysis shows that such a counterintuitive behavior could be easily explained from the fact that the survival probability distribution is governed by two distinct phases, corresponding to two separate time exponents, for the two different time regimes. The relatively shorter timescales correspond to the cohesion:adhesion induced immature bond formation whereas the larger time reciprocates the association:dissociation regime leading to TCR:pMHC signaling. From an estimate of the bond survival probability, we show that, at shorter timescales, this probability P(Δ)(τ) scales with time τ as a universal function of a rescaled noise amplitude D/Δ(2), such that P(Δ)(τ)∼τ(-(Δ/√[D]+1/2)),Δ being the distance from the mean intermembrane (T cell:Antigen Presenting Cell) separation distance. The crossover from this shorter to a longer time regime leads to a universality in the dynamics, at which point the survival probability shows a different power-law scaling compared to the one at shorter timescales. In biological terms, such a crossover indicates that the TCR:pMHC bond has a survival probability with a slower decay rate than the longer LFA-1:ICAM-1 bond justifying its stability.

The origin of linear instability resulting in rotating sheared accretion flows has remained a controversial subject for a long time. While some explanations of such non-normal transient growth of disturbances in the Rayleigh stable limit were available for magnetized accretion flows, similar instabilities in the absence of magnetic perturbations remained unexplained. This dichotomy was resolved in two recent publications by Chattopadhyay and co-workers [Mukhopadhyay and Chattopadhyay, J. Phys. A 46, 035501 (2013); Nath et al., Phys. Rev. E 88, 013010 (2013)] where it was shown that such instabilities, especially for nonmagnetized accretion flows, were introduced through interaction of the inherent stochastic noise in the system (even a "cold" accretion flow at 3000 K is too "hot" in the statistical parlance and is capable of inducing strong thermal modes) with the underlying Taylor-Couette flow profiles. Both studies, however, excluded the additional energy influx (or efflux) that could result from nonzero cross correlation of a noise perturbing the velocity flow, say, with the noise that is driving the vorticity flow (or equivalently the magnetic field and magnetic vorticity flow dynamics). Through the introduction of such a time symmetry violating effect, in this article we show that nonzero noise cross correlations essentially renormalize the strength of temporal correlations. Apart from an overall boost in the energy rate (both for spatial and temporal correlations, and hence in the ensemble averaged energy spectra), this results in mutual competition in growth rates of affected variables often resulting in suppression of oscillating Alfven waves at small times while leading to faster saturations at relatively longer time scales. The effects are seen to be more pronounced with magnetic field fluxes where the noise cross correlation magnifies the strength of the field concerned. Another remarkable feature noted specifically for the autocorrelation functions is the removal of energy degeneracy in the temporal profiles of fast growing non-normal modes leading to faster saturation with minimum oscillations. These results, including those presented in the previous two publications, now convincingly explain subcritical transition to turbulence in the linear limit for all possible situations that could now serve as the benchmark for nonlinear stability studies in Keplerian accretion disks.

This paper resolves the long standing debate as to the proper time scale 〈τ〉 of the onset of the immunological synapse bond, the noncovalent chemical bond defining the immune pathways involving T cells and antigen presenting cells. Results from our model calculations show 〈τ〉 to be of the order of seconds instead of minutes. Close to the linearly stable regime, we show that in between the two critical spatial thresholds defined by the integrin:ligand pair (Δ2∼ 40-45 nm) and the T-cell receptor TCR:peptide-major-histocompatibility-complex pMHC bond (Δ1∼ 14-15 nm), 〈τ〉 grows monotonically with increasing coreceptor bond length separation δ (= Δ2-Δ1∼ 26-30 nm) while 〈τ〉 decays with Δ1 for fixed Δ2. The nonuniversal δ-dependent power-law structure of the probability density function further explains why only the TCR:pMHC bond is a likely candidate to form a stable synapse.

A one-pot, mild, two-component iminium ion-enamine cascade reaction to construct structurally diverse azacyclic frameworks from l-proline and l-pipecolic acid, and its application to indolizidine and quinolizidine alkaloids and azasteroids, is reported.

Biosynthetically and chemically derived analogs of the antibiotic pactamycin and de-6-methylsalicylyl (MSA)-pactamycin have attracted recent interest as potential antiprotozoal and antitumor drugs. Here, we report a 3.1-Å crystal structure of de-6-MSA-pactamycin bound to its target site on the Thermus thermophilus 30S ribosomal subunit. Although de-6-MSA-pactamycin lacks the MSA moiety, it shares the same binding site as pactamycin and induces a displacement of nucleic acid template bound at the E-site of the 30S. The structure highlights unique interactions between this pactamycin analog and the ribosome, which paves the way for therapeutic development of related compounds., (Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

We investigate the evolution of magnetohydrodynamic (or hydromagnetic as coined by Chandrasekhar) perturbations in the presence of stochastic noise in rotating shear flows. The particular emphasis is the flows whose angular velocity decreases but specific angular momentum increases with increasing radial coordinate. Such flows, however, are Rayleigh stable but must be turbulent in order to explain astrophysical observed data and, hence, reveal a mismatch between the linear theory and observations and experiments. The mismatch seems to have been resolved, at least in certain regimes, in the presence of a weak magnetic field, revealing magnetorotational instability. The present work explores the effects of stochastic noise on such magnetohydrodynamic flows, in order to resolve the above mismatch generically for the hot flows. We essentially concentrate on a small section of such a flow which is nothing but a plane shear flow supplemented by the Coriolis effect, mimicking a small section of an astrophysical accretion disk around a compact object. It is found that such stochastically driven flows exhibit large temporal and spatial autocorrelations and cross-correlations of perturbation and, hence, large energy dissipations of perturbation, which generate instability. Interestingly, autocorrelations and cross-correlations appear independent of background angular velocity profiles, which are Rayleigh stable, indicating their universality. This work initiates our attempt to understand the evolution of three-dimensional hydromagnetic perturbations in rotating shear flows in the presence of stochastic noise.

We present a data based statistical study on the effects of seasonal variations in the growth rates of the gastro-intestinal (GI) parasitic infection in livestock. The alluded growth rate is estimated through the variation in the number of eggs per gram (EPG) of faeces in animals. In accordance with earlier studies, our analysis too shows that rainfall is the dominant variable in determining EPG infection rates compared to other macro-parameters like temperature and humidity. Our statistical analysis clearly indicates an oscillatory dependence of EPG levels on rainfall fluctuations. Monsoon recorded the highest infection with a comparative increase of at least 2.5 times compared to the next most infected period (summer). A least square fit of the EPG versus rainfall data indicates an approach towards a super diffusive (i. e. root mean square displacement growing faster than the square root of the elapsed time as obtained for simple diffusion) infection growth pattern regime for low rainfall regimes (technically defined as zeroth level dependence) that gets remarkably augmented for large rainfall zones. Our analysis further indicates that for low fluctuations in temperature (true on the bulk data), EPG level saturates beyond a critical value of the rainfall, a threshold that is expected to indicate the onset of the nonlinear regime. The probability density functions (PDFs) of the EPG data show oscillatory behavior in the large rainfall regime (greater than 500 mm), the frequency of oscillation, once again, being determined by the ambient wetness (rainfall, and humidity). Data recorded over three pilot projects spanning three measures of rainfall and humidity bear testimony to the universality of this statistical argument.

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

We study the comparative importance of thermal to nonthermal fluctuations for membrane-based models in the linear regime. Our results, both in 1+1 and 2+1 dimensions, suggest that nonthermal fluctuations dominate thermal ones only when the relaxation time τ is large. For moderate to small values of τ, the dynamics is defined by a competition between these two forces. The results are expected to act as a quantitative benchmark for biological modeling in systems involving cytoskeletal and other nonthermal fluctuations.

Childhood disintegrative disorder (CDD) (ICD-10 F84.3) is defined by a period of normal development before onset followed by gradual loss of previously acquired skills with the development of characteristic abnormalities of social, communicative and behavioural functioning. We report two children with apparent CDD, who showed amelioration of behaviour, language and motor regression after corticosteroid treatment.

We study memory effects in a kinetic roughening model. For d=1, a different dynamic scaling is uncovered in the memory dominated phases; the Kardar-Parisi-Zhang scaling is restored in the absence of noise. dc=2 represents the critical dimension where memory is shown to smoothen the roughening front (alpha

Cilia and flagella are hairlike extensions of eukaryotic cells which generate oscillatory beat patterns that can propel micro-organisms and create fluid flows near cellular surfaces. The evolutionary highly conserved core of cilia and flagella consists of a cylindrical arrangement of nine microtubule doublets, called the axoneme. The axoneme is an actively bending structure whose motility results from the action of dynein motor proteins cross-linking microtubule doublets and generating stresses that induce bending deformations. The periodic beat patterns are the result of a mechanical feedback that leads to self-organized bending waves along the axoneme. Using a theoretical framework to describe planar beating motion, we derive a nonlinear wave equation that describes the fundamental Fourier mode of the axonemal beat. We study the role of nonlinearities and investigate how the amplitude of oscillations increases in the vicinity of an oscillatory instability. We furthermore present numerical solutions of the nonlinear wave equation for different boundary conditions. We find that the nonlinear waves are well approximated by the linearly unstable modes for amplitudes of beat patterns similar to those observed experimentally.

The present paper is an attempt for unifying two different quinoxaline data sets with a wide range of substituents in 2, 3, 7, and 8 positions having excellent antitubercular activities with a view to developing robust and reliable structure-activity relationships. The merging has been performed for these two sets of quinoxaline 1,4-di-N-oxides derivatives comprising 29 and 18 compounds, respectively, on the basis of constitutional descriptors, which denotes the structural characterization of the molecules. Principal component analysis was performed to see the distribution of the compounds from two data sets for the constitutional descriptors. The distribution of compounds in score plot based on constitutional descriptors suggests unification of quinoxaline data sets which is useful for the model development. Outlier detection was performed from the standpoint of residual analysis of the partial least squares regression models. The superiority of the constitutional descriptors over other calculated molecular descriptors has been established from the standpoint of leave-one-out cross-validation technique associated with partial least squares regression analysis. Internal validation through the leave-many-out methodology was also performed with good results, assuring the stability of the models. The results obtained from linear partial least squares regression analysis lead to a statistically significant and robust quantitative structure-activity relationship modeling.

A convergent total synthesis of the cytotoxic natural product cruentaren B is completed in 26 steps (longest linear sequence) with an overall yield of 7.1%. For the construction of the C1-C11 benzolactone fragment of the molecule, the key steps used were O-methylation, using a Mitsunobu reaction, a Stille coupling method to construct the C7-C8 bond, and a Brown's asymmetric crotylboration reaction for the direct enantioselective installation of the two chiral centers present in this fragment. For diastereoselective installation of the chiral centers in the C12-C20 polyketide fragment, an Evans syn aldol reaction on a chiral aldehyde, derived from methyl (R)-3-hydroxyl-2-methylpropionate, and subsequently a Mukaiyama aldol reaction were employed. For the construction of the C21-C28 tail, a "non-Evans" syn aldol reaction was used. The three fragments were coupled by an SN2 reaction and a Wittig olefination reaction followed by standard functional group manipulations to furnish the target molecule.

We study the strong coupling (SC) limit of the anisotropic Kardar-Parisi-Zhang (KPZ) model. A systematic mapping of the continuum model to its lattice equivalent shows that in the SC limit, anisotropic perturbations destroy all spatial correlations but retain a temporal scaling which shows a remarkable crossover along one of the two spatial directions, the choice of direction depending on the relative strength of anisotropicity. The results agree with exact numerics and are expected to settle the long-standing SC problem of a KPZ model in the infinite range limit.

Ataxia and peripheral neuropathy are the most common neurological manifestations of gluten sensitivity. Myopathy is a less common and poorly characterized additional neurological manifestation of gluten sensitivity. We present our experience with 13 patients who presented with symptoms and signs suggestive of a myopathy and in whom investigation led to the diagnosis of gluten sensitivity. Three of these patients had a neuropathy with or without ataxia in addition to the myopathy. The mean age at onset of the myopathic symptoms was 54 years. Ten patients had neurophysiological evidence of myopathy. Inflammatory myopathy was the most common finding on neuropathological examination. One patient had basophilic rimmed vacuoles suggestive of inclusion-body myositis. Six patients received immunosuppressive treatment in addition to starting on a gluten-free diet; five improved and one remained unchanged. Among seven patients not on immunosuppressive treatment, four showed clinical improvement of the myopathy with a gluten-free diet. The improvement was also associated with reduction or normalization of serum creatine kinase level. The myopathy progressed in one patient who refused the gluten-free diet. Myopathy may be another manifestation of gluten sensitivity and is likely to have an immune-mediated pathogenesis. A gluten-free diet may be a useful therapeutic intervention.

We study the dynamics of a polymer of varying stiffness, pinned or grafted at both ends and subjected to an oscillatory forcing at an intermediate point. Via stochastic simulations, we find a crossover from a periodic limit cycle to an aperiodic dynamics as the polymer gets "stiffer." An analytical argument valid in the 2D grafted case shows that in such a case this aperiodic dynamics has some chaotic signatures.

We studied the effect of a gluten-free diet in patients with idiopathic sensorimotor axonal neuropathy and circulating antigliadin antibodies. Consecutive patients underwent baseline neurophysiological assessment and were offered a gluten-free diet. Those who went on the diet formed the intention-to-treat group and those who did not were the control group. Repeat neurophysiological assessment and subjective evaluation of neuropathy symptoms were performed at 1 year. A total of 35 patients participated in the study, with 25 patients going on the diet and 10 not doing so. There was a significant difference in the change of sural sensory action potentials (pre-defined primary endpoint), with evidence of improvement in the intention-to-treat group and deterioration in the control group. Subjective change in neuropathy symptoms also showed significant differences, with patients in the intention-to-treat group reporting improvement and those in the control group reporting deterioration. Gluten-free diet may thus be a useful therapeutic intervention for patients with gluten neuropathy.