New Ways to Tackle Malaria

Malaria is one of the oldest tropical diseases and still remains a focus of attention. Sub-Saharan African countries contribute 90% of the total malaria cases in the world. The World Health Organization (WHO) has advocated eliminating this disease by 2030 with the existing strategies and tools. Many initiatives are underway by several organizations, and 38 countries have achieved the elimination goal. The main backbone of the elimination process is smart surveillance followed by prompt public health responses. The control of the disease mainly relies on treatment of malaria positive cases with anti-malarials namely artemisinin-based combination therapy (ACT) for Plasmodium falciparum. In India, chloroquine is still effective against P. vivax. Use of 8-aminoquinolines primaquine and more recently tefenoquine warrants testing of G6PD deficiency status to avoid unnecessary hemolysis. Vector control operations mainly depend on the use of long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) with insecticides. The threat of resistance draws an open challenge in both treatment and vector management. New initiatives on surveillance, treatment, chemoprevention, and vector control using modern techniques of artificial intelligence, machine learning, genetic engineering, and digital approach of community engagement have great potential to accelerate the malaria elimination process

A Jensen-Shannon Divergence Based Loss Function for Bayesian Neural Networks

Kullback-Leibler (KL) divergence is widely used for variational inference of Bayesian Neural Networks (BNNs). However, the KL divergence has limitations such as unboundedness and asymmetry. We examine the Jensen-Shannon (JS) divergence that is more general, bounded, and symmetric. We formulate a novel loss function for BNNs based on the geometric JS divergence and show that the conventional KL divergence-based loss function is its special case. We evaluate the divergence part of the proposed loss function in a closed form for a Gaussian prior. For any other general prior, Monte Carlo approximations can be used. We provide algorithms for implementing both of these cases. We demonstrate that the proposed loss function offers an additional parameter that can be tuned to control the degree of regularisation. We derive the conditions under which the proposed loss function regularises better than the KL divergence-based loss function for Gaussian priors and posteriors. We demonstrate performance improvements over the state-of-the-art KL divergence-based BNN on the classification of a noisy CIFAR data set and a biased histopathology data set.Comment: To be submitted for peer review in IEE

An atomistic-based foliation model for multilayer graphene materials and nanotubes

We present a three-dimensional continuum model for layered crystalline materials made out of weakly interacting two-dimensional crystalline sheets. We specialize the model to multilayer graphene materials, including multi-walled carbon nanotubes (MWCNTs). We view the material as a foliation, partitioning of space into a continuous stack of leaves, thus loosing track of the location of the individual graphene layers. The constitutive model for the bulk is derived from the atomistic interactions by appropriate kinematic assumptions, adapted to the foliation structure and mechanics. In particular, the elastic energy along the leaves of the foliation results from the bonded interactions, while the interaction energy between the walls, resulting from van der Waals forces, is parametrized with a stretch transversal to the foliation. The resulting theory is distinct from conventional anisotropic models, and can be readily discretized with finite elements. The discretization is not tied to the individual walls and allows us to coarse-grain the system in all directions. Furthermore, the evaluation of the non-bonded interactions becomes local. We test the accuracy of the foliation model against a previously proposed atomistic-based continuum model that explicitly describes each and every wall. We find that the new model is very efficient and accurate. Furthermore, it allows us to rationalize the rippling deformation modes characteristic of thick MWCNTs, highlighting the role of the van der Waals forces and the sliding between the walls. By exercising the model with very large systems of hollow MWCNTs and suspended multilayer graphene, containing up to 109 atoms, we find new complex post-buckling deformation patterns.Peer ReviewedPostprint (author's final draft

Studies on Some N-Acyl-N-phenylhydroxylamines as Metal Complexing Ligands: Part X - Formation Constants of Fe(III) Complexes of Some N-Hydroxysuccinamic Acids

413-41

Bayesian Calibration and Uncertainty Quantification of a Rate-dependent Cohesive Zone Model for Polymer Interfaces

In the present work, a rate-dependent cohesive zone model for the fracture of polymeric interfaces is presented. Inverse calibration of parameters for such complex models through trial and error is computationally tedious due to the large number of parameters and the high computational cost associated. The obtained parameter values are often non-unique and the calibration inherits higher uncertainty when the available experimental data is limited. To alleviate these difficulties, a Bayesian calibration approach is used for the proposed rate-dependent cohesive zone model in this work. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced and it is approximated as a Gaussian process. The improvement in the model predictions following the introduction of a discrepancy function is demonstrated justifying the need for a discrepancy term. Finally, the overall uncertainty of the model is quantified in a predictive setting and the results are provided as confidence intervals. A sensitivity analysis is also performed to understand the effect of the variability of the inputs on the nature of the output.Comment: To be submitted for peer-revie

Construction of kernel for nonlocal elasticity from one-dimensional dispersion data in reciprocal space

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106461/1/AIAA2013-1508.pd

Innovations in Vector-Borne Disease Control in India

India is the second largest populous and democratic country in the world. Several geo-ecological settings are favorable for most of the vector borne diseases (VBDs) in the country. Malaria, Lymphatic Filariasis (LF), Japanese Encephalitis (JE), Dengue (DEN), Chikungunya (CHIK) and Kala-azar (KA) are major VBDs. Kyasanur Forest Disease (KFD), Plague and Chandipura virus (CHPV) infections have limited and localized foci, but needs attention. Crimean-Congo Hemorrhagic Fever (CCHF) and Zika are recent entries in India that also need to be handled on priority. National Vector Borne Disease Control Program (NVBDCP) is responsible for control and prevention of all these diseases. Malaria, LF, JE, DEN, CHIK and Zika are transmitted by different species of mosquitoes. KA and CHPV are transmitted by shadflies, while KFD, CCHF by ticks; plague by fleas. Scrub typhus (ST) responsible for acute encephalopathy syndrome (AES) is transmitted by Leptotrombidium mite species. It needs specific and strategic action plan in view of the diversified biodiversity. New innovations to strengthen the public health responses are the main intervention protocols. Already two diseases Guineaworm (Dracunculiasis) transmitted by different species of Cyclops, and polio have been successfully eradicated/eliminated from India. Such experience would be very helpful for the elimination of malaria, LF and KA, and all are on the elimination drive

New Challenges in Malaria Elimination

In recent years, efforts to eliminate malaria has gained a tremendous momentum, and many countries have achieved this goal — but it has faced many challenges. Recent COVID-19 pandemic has compounded the challenges due to cessation of many on-field operations. Accordingly, the World Health Organization (WHO) has advocated to all malaria-endemic countries to continue the malaria elimination operations following the renewed protocols. The recent reports of artemisinin resistance in Plasmodium falciparum followed by indication of chloroquine resistance in P. vivax, and reduced susceptibility of synthetic pyrethroids used in long lasting insecticide nets are some issues hindering the elimination efforts. Moreover, long distance night migration of vector mosquitoes in sub-Saharan Africa and invasion of Asian vector Anopheles stephensi in many countries including Africa and Southeast Asia have added to the problems. In addition, deletion of histidine rich protein 2 and 3 (Pfhrp2/3) genes in P. falciparum in many countries has opened new vistas to be addressed for point-of-care diagnosis of this parasite. It is needed to revisit the strategies adopted by those countries have made malaria elimination possible even in difficult situations. Strengthening surveillance and larval source management are the main strategies for successful elimination of malaria. New technologies like Aptamar, and artificial intelligence and machine learning would prove very useful in addressing many ongoing issues related to malaria elimination

Effect of Electron-Phonon Scattering on the Anomalous Hall Conductivity of Fe3_3Sn: A Kagome Ferromagnetic Metal

We report on magnetic and magnetotransport studies of a Kagome ferromagnetic metal, Fe$_3$Sn. Our studies reveal a large anomalous Hall conductivity ($\sigma_{zx}$) in this system, mainly contributed by temperature independent intrinsic Hall conductivity ($\sigma^{int}_{zx}$=485$\pm$60 S/cm) and temperature dependent extrinsic Hall conductivity ($\sigma^{ext}_{zx}$) due to skew-scattering. Although $\sigma^{ext}_{zx}$ value is large and almost equivalent to the intrinsic Hall conductivity at low temperatures, it drastically decreases with increasing temperature, following the relation $\sigma^{ext}_{zx}=\frac{\sigma_{zx0}^{ext}}{(aT+1)^2}$, under the influence of electron-phonon scattering. The presence of electron-phonon scattering in this system is also confirmed by the linear dependence of longitudinal electrical resistivity at higher temperatures [$\rho(T)\propto T$]. We further find that Fe$_3$Sn is a soft ferromagnet with an easy-axis of magnetization lying in the $\it{ab}$ plane of the crystal with magnetocrystalline anisotropy energy density as large as 1.02 $\times$ 10$^6$Comment: 8 pages and 4 figures, accepted in Phys. Rev.