Birla Institute of Technology and Science, Pilani - Goa Campus

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

After the 1918 flu pandemic, the world is again facing a similar situation. However, the advancement in medical science has made it possible to identify that the novel infectious agent is from the coronavirus family. Rapid genome sequencing by various groups helped in identifying the structure and function of the virus, its immunogenicity in diverse populations, and potential preventive measures. Coronavirus attacks the respiratory system, causing pneumonia and lymphopenia in infected individuals. Viral components like spike and nucleocapsid proteins trigger an immune response in the host to eliminate the virus. These viral antigens can be either recognized by the B cells or presented by MHC complexes to the T cells, resulting in antibody production, increased cytokine secretion, and cytolytic activity in the acute phase of infection. Genetic polymorphism in MHC enables it to present some of the T cell epitopes very well over the other MHC alleles. The association of MHC alleles and its downregulated expression has been correlated with disease severity against influenza and coronaviruses. Studies have reported that infected individuals can, after recovery, induce strong protective responses by generating a memory T-cell pool against SARS-CoV and MERS-CoV. These memory T cells were not persistent in the long term and, upon reactivation, caused local damage due to cross-reactivity. So far, the reports suggest that SARS-CoV-2, which is highly contagious, shows related symptoms in three different stages and develops an exhaustive T-cell pool at higher loads of viral infection. As there are no specific treatments available for this novel coronavirus, numerous small molecular drugs that are being used for the treatment of diseases like SARS, MERS, HIV, ebola, malaria, and tuberculosis are being given to COVID-19 patients, and clinical trials for many such drugs have already begun. A classical immunotherapy of convalescent plasma transfusion from recovered patients has also been initiated for the neutralization of viremia in terminally ill COVID-19 patients. Due to the limitations of plasma transfusion, researchers are now focusing on developing neutralizing antibodies against virus particles along with immuno-modulation of cytokines like IL-6, Type I interferons (IFNs), and TNF-α that could help in combating the infection. This review highlights the similarities of the coronaviruses that caused SARS and MERS to the novel SARS-CoV-2 in relation to their pathogenicity and immunogenicity and also focuses on various treatment strategies that could be employed for curing COVID-19.

Abstract UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

Global mechanization, urbanization, and various natural processes have led to the increased release of toxic compounds into the biosphere. These hazardous toxic pollutants include a variety of organic and inorganic compounds, which pose a serious threat to the ecosystem. The contamination of soil and water are the major environmental concerns in the present scenario. This leads to a greater need for remediation of contaminated soils and water with suitable approaches and mechanisms. The conventional remediation of contaminated sites commonly involves the physical removal of contaminants, and their disposition. Physical remediation strategies are expensive, non-specific and often make the soil unsuitable for agriculture and other uses by disturbing the microenvironment. Owing to these concerns, there has been increased interest in eco-friendly and sustainable approaches such as bioremediation, phytoremediation and rhizoremediation for the cleanup of contaminated sites. This review lays particular emphasis on biotechnological approaches and strategies for heavy metal and metalloid containment removal from the environment, highlighting the advances and implications of bioremediation and phytoremediation as well as their utilization in cleaning-up toxic pollutants from contaminated environments.

The sessile nature of plants' life is endowed with a highly evolved defense system to adapt and survive under environmental extremes. To combat such stresses, plants have developed complex and well-coordinated molecular and metabolic networks encompassing genes, metabolites, and acclimation responses. These modulate growth, photosynthesis, osmotic maintenance, and carbohydrate homeostasis. Under a given stress condition, sugars act as key players in stress perception, signaling, and are a regulatory hub for stress-mediated gene expression ensuring responses of osmotic adjustment, scavenging of reactive oxygen species, and maintaining the cellular energy status through carbon partitioning. Several sugar transporters are known to regulate carbohydrate partitioning and key signal transduction steps involved in the perception of biotic and abiotic stresses. Sugar transporters such as SUGARS WILL EVENTUALLY BE EXPORTED TRANSPORTER (SWEETs), SUCROSE TRANSPORTERS (SUTs), and MONOSACCHARIDE TRANSPORTERS (MSTs) are involved in sugar loading and unloading as well as long-distance transport (source to sink) besides orchestrating oxidative and osmotic stress tolerance. It is thus necessary to understand the structure-function relationship of these sugar transporters to fine-tune the abiotic stress-modulated responses. Advances in genomics have unraveled many sugars signaling components playing a key role in cross-talk in abiotic stress pathways. An integrated omics approach may aid in the identification and characterization of sugar transporters that could become targets for developing stress tolerance plants to mitigate climate change effects and improve crop yield. In this review, we have presented an up-to-date analysis of the sugar homeostasis under abiotic stresses as well as describe the structure and functions of sugar transporters under abiotic stresses.

A stimulated Raman scattering microscope with near-infrared picosecond laser pulses at high repetition rates (76 MHz) and radio-frequency lock-in detection is accomplished. Based on stimulated Raman loss detection, we demonstrate noninvasive point-by-point vibrational mapping of chemical and biological samples with high sensitivity and without the requirement for labeling of the sample with natural or artificial fluorophores. We experimentally demonstrate a major benefit of this technique, which is the capability to respond exclusively to the linear Raman-resonance properties of the sample, thus allowing a direct quantitative interpretation of image contrast in terms of the number density of Raman-active modes.

Plants, as sessile organisms experience various abiotic stresses, which pose serious threat to crop production. Plants adapt to environmental stress by modulating their growth and development along with the various physiological and biochemical changes. This phenotypic plasticity is driven by the activation of specific genes encoding signal transduction, transcriptional regulation, ion transporters and metabolic pathways. Rice is an important staple food crop of nearly half of the world population and is well known to be a salt sensitive crop. The completion and enhanced annotations of rice genome sequence has provided the opportunity to study functional genomics of rice. Functional genomics aids in understanding the molecular and physiological basis to improve the salinity tolerance for sustainable rice production. Salt tolerant transgenic rice plants have been produced by incorporating various genes into rice. In this review we present the findings and investigations in the field of rice functional genomics that includes supporting genes and networks (ABA dependent and independent), osmoprotectants (proline, glycine betaine, trehalose, myo-inositol, and fructans), signaling molecules (Ca2+, abscisic acid, jasmonic acid, brassinosteroids) and transporters, regulating salt stress response in rice.

The properties of the supercritical fluid near the critical point can be changed by varying the pressure and temperature, allowing selective and faster extraction. Supercritical fluids exhibit liquid-like density and gas – like viscosity & diffusion coefficients and can penetrate more into the solid matrix inaccessible to liquids due to negligible surface tension and viscosity. They are suitable as a substitute for organic solvents in a range of industrial and laboratory processes. These properties of supercritical fluids are well suited for the extraction of solutes, from various plants, fruits, flowers, seeds, leaves, which cannot be easily extracted using conventional extraction processes. In view of above mentioned advantages, supercritical fluid extraction is becoming increasingly popular in many industries such as petroleum, chemical, food, and perfumery. Supercritical fluids are also used for the extraction of metal ions from aqueous solutions and solid and liquid matrices.This review paper provides a concise review of the applications of supercritical fluids from the perspective of feed materials, type of supercritical fluids, co solvents and modifiers used, nature of work, operating conditions, findings, limitations of the work done and scope for further research. The application of supercritical fluids in extraction and purification of various natural extracts and others, using CO2 accounts for more than 90% of the published research in the field of supercritical fluid technology. Other supercritical fluids being used are propane, hexane and butane. Carbon dioxide is a relatively non-polar solvent but has some limited affinity with polar molecules due to its large molecular quadrupole. Co-solvents and modifiers are often be added to improve the solubility of polar molecules. On addition of various co solvents and modifier such as isopropyl alcohol and ethanol, n-hexane, heptane, pentane, toluene, methanol, acetone, formic acid with ammonium formate in methanol etc enhances the performance of supercritical extraction process. Despite this, the status of supercritical fluid technology commercialization is less than satisfactory. A review studies revealed that most of the published research is at laboratory scale with little or no information on scale up and design strategies. Keywords: Supercritical fluids, Carbon dioxide, Co solvents, Modifier, Extraction

Abstract Polybenzoxazines are newly developed thermoset polymers exhibiting versatility in a wide range of applications including in the electronics and aerospace industries. When combined as composites, the attractive characteristics of both components are apparent. The chemistry of benzoxazine synthesis offers wide molecular design flexibility and thus facilitates preparation of various polybenzoxazine‐based composites. This article reviews recent developments in the preparation and thermal curing of benzoxazine composites with a focus on structure–property relations of cured materials. Copyright © 2010 Society of Chemical Industry

Plant mineral nutrition is important for obtaining higher agricultural productivity to meet the future demands of the increasing global human population. It is envisaged that nanotechnology can provide sustainable solutions by replacing traditional bulk fertilizers with their nanoparticulate counterparts possessing superior properties to overcome the current challenges of bioavailability and uptake of minerals, increasing crop yield, reducing fertilizer wastage, and protecting the environment. Recent studies have shown that nanoparticles of essential minerals and nonessential elements affect plant growth, physiology, and development, depending on their size, composition, concentration, and mode of application. The current review includes the recent findings on the positive as well as negative effects that nanofertilizers exert on plants when applied via foliar and soil routes, their effects on plant associated microorganisms, and potential for controlling agricultural pests. This review suggests future research needed for the development of sustained release nanofertilizers for enhancing food production and environmental protection.

Nanostructured materials have gained immense attraction because of their extraordinary properties compared to the bulk materials to be used in a plethora of applications in myriad fields. In this review article, we have discussed how the Density Functional Theory (DFT) calculation can be used to explain some of the properties of nanomaterials. With some specific examples here, it has been shown that how closely the different properties of nanomaterials (such as optical, optoelectronics, catalytic and magnetic) predicted by DFT calculations match well with the experimentally determined values. Some examples were discussed in detail to inspire the experimental scientists to conduct DFT-based calculations along with the experiments to derive a better understanding of the experimentally obtained results as well as to predict the properties of the nanomaterial. We have pointed out the challenges associated with DFT, and potential future perspectives of this new exciting field.

OMA is an established resource to elucidate evolutionary relationships among genes from currently 2326 genomes covering all domains of life. OMA provides pairwise and groupwise orthologs, functional annotations, local and global gene order conservation (synteny) information, among many other functions. This update paper describes the reorganisation of the database into gene-, group- and genome-centric pages. Other new and improved features are detailed, such as reporting of the evolutionarily best conserved isoforms of alternatively spliced genes, the inferred local order of ancestral genes, phylogenetic profiling, better cross-references, fast genome mapping, semantic data sharing via RDF, as well as a special coronavirus OMA with 119 viruses from the Nidovirales order, including SARS-CoV-2, the agent of the COVID-19 pandemic. We conclude with improvements to the documentation of the resource through primers, tutorials and short videos. OMA is accessible at https://omabrowser.org.

The permutational invariance of identical two-level systems allows for an exponential reduction in the computational resources required to study the Lindblad dynamics of coupled spin-boson ensembles evolving under the effect of both local and collective noise. Here we take advantage of this speedup to study several important physical phenomena in the presence of local incoherent processes, in which each degree of freedom couples to its own reservoir. Assessing the robustness of collective effects against local dissipation is paramount to predict their presence in different physical implementations. We have developed an open-source library in python, the Permutational-Invariant Quantum Solver (PIQS), which we use to study a variety of phenomena in driven-dissipative open quantum systems. We consider both local and collective incoherent processes in the weak-, strong-, and ultrastrong-coupling regimes. Using PIQS, we reproduce a series of known physical results concerning collective quantum effects and extend their study to the local driven-dissipative scenario. Our work addresses the robustness of various collective phenomena, e.g., spin squeezing, superradiance, and quantum phase transitions, against local dissipation processes.

In this work, we have reported a nanocomposite, composed of a BiFeO 3 nanowire and reduced graphene oxide (BFO-RGO), as an electrode material for a high-performance supercapacitor. A facile hydrothermal method was employed to prepare BFO-RGO nanocomposite. The electrochemical measurements were performed by cyclic voltammetry, galvanostatic charge/discharge measurements, and electrochemical impedance spectroscopy. The specific capacitance of the BFO-RGO nanocomposite was 928.43 F g –1 at current density 5 A g –1, which is superior to that of pure BiFeO 3 . Additionally, this nanocomposite shows good cyclic stability, and ∼87.51% of specific capacitance is retained up to 1000 cycles. It also exhibits a high charge density of 18.62 W h kg –1 when the power density is 950 W kg –1 . These attractive results suggest the potential of BiFeO 3 nanowire-RGO nanocomposite as an active material for the construction of a high-performance supercapacitor electrode. To the best of our knowledge, this is the first time the application of BiFeO 3 nanowire-RGO nanocomposite as a supercapacitor has been reported.

Biosynthesis of nanoparticles using microorganisms has attracted a lot of attention in recent years as this route has the potential to lead to synthesis of monodisperse nanoparticles. Here, we report the intracellular synthesis of stable lead sulfide nanoparticles by a marine yeast, Rhodosporidium diobovatum. The PbS nanoparticles were characterized by UV-visible absorption spectroscopy, X-ray diffraction (XRD) and energy dispersive atomic spectroscopy (EDAX). UV-visible absorption scan revealed a peak at 320 nm, a characteristic of the nanosize range. XRD confirmed the presence of PbS nanoparticles of cubic structure. Crystallite size as determined from transmission electron microscopy was found to be in the range of 2-5 nm. Elemental analysis by EDAX revealed the presence of particles composed of lead and sulfur in a 1:2 ratio indicating that PbS nanoparticles were capped by a sulfur-rich peptide. A quantitative study of lead uptake through atomic absorption spectrometry revealed that 55% of lead in the medium was accumulated in the exponential phase, whereas a further 35% was accumulated in the stationary phase; thus, the overall recovery of PbS nanoparticles was 90%. The lead-exposed yeast displayed a marked increase (280% over the control) in nonprotein thiols in the stationary phase.

Purpose The purpose of this paper is to identify and examine the important factors that could affect consumers' behavioural intention and use behaviour towards mobile payment services during COVID-19. Design/methodology/approach The proposed model extends meta-Unified Theory of Acceptance and Use of Technology (meta-UTAUT) model with perceived severity and self-efficacy factors affecting consumers' behavioural intention and use behaviour towards mobile payment services. A convenient sampling technique has been utilized to gather data from a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation. Findings The findings revealed that performance expectancy, effort expectancy and perceived severity have a significant positive impact on consumers' attitude; facilitating conditions has a significant positive impact on effort expectancy; self-efficacy has a significant positive impact on effort expectancy; attitude has a significant positive impact on behavioural intention; and behavioural intention has a significant positive impact on use behaviour. Social influence did not confirm any significant relationship. Research limitations/implications The current research study has utilized a non-probability convenient sampling technique to gather data through a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation. The respondents were adopters of mobile payment services. The scope of the study is the COVID-19 context or related chronic diseases context where major preventive mechanisms such as social distancing and avoidance of physical contacts are vital. Originality/value This study has extended the meta-UTAUT model with the COVID-19 context-specific constructs and relationships. The undertaken work has strengthened the explanability of the model. The inclusion of context relevant variables such as perceived severity and self-efficacy and their association with the existing meta-UTAUT framework have enriched the context of the study. The current study offers a holistic understanding of significant factors influencing Indian consumers’ adoption of mobile payment services in the COVID-19 context.

BACKGROUND: The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence. RESULTS: LocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana). Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot. CONCLUSIONS: LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot.

We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

) as concluded from the nitroblue tetrazolium probe test and photoluminescence experiment. It is hoped that the exceptional photocatalytic performance of our work makes the conducting polymer-based composite an effective alternative in wastewater treatment for industrial applications.

Today in sub-nanometer regime, chip/system designers add accuracy as a new constraint to optimize Latency-Power-Area (LPA) metrics. In this paper, we present a new power and area-efficient Approximate Wallace Tree Multiplier (AWTM) for error-tolerant applications. We propose a bit-width aware approximate multiplication algorithm for optimal design of our multiplier. We employ a carry-in prediction method to reduce the critical path. It is further augmented with hardware efficient precomputation of carry-in. We also optimize our multiplier design for latency, power and area using Wallace trees. Accuracy as well as LPA design metrics are used to evaluate our approximate multiplier designs of different bit-widths, i.e. 4 × 4, 8 × 8 and 16 × 16. The simulation results show that we obtain a mean accuracy of 99.85% to 99.965%. Single cycle implementation of AWTM gives almost 24% reduction in latency. We achieve significant reduction in power and area, i.e. up to 41.96% and 34.49% respectively that clearly demonstrates the merits of our proposed AWTM design. Finally, AWTM is used to perform a real time application on a benchmark image. We obtain up to 39% reduction in power and 30% reduction in area without any loss in image quality.