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Lignin Valorisation Using Lignolytic Microbes and Enzymes: Challenges and Opportunities
No full textLignin is an abundant aromatic biopolymer. It hinders the extraction and degradation of cellulose and hemicellulose and the subsequent biochemical conversion of lignocellulosic wastes to fuels and chemicals. A substantial amount of research has been conducted over the past 20 years to develop novel and effective techniques to extract and recover valuable compounds of lignin. However, because of the extremely recalcitrant nature of lignin, the development of a suitable, cost-effective and efficient method of extracting lignin has been difficult. Using microbes and enzymes for the depolymerisation of lignin is an energy-efficient method. Numerous studies have been performed to develop a suitable bacterial strain that can be used to recycle all lignin-derived compounds, but designing such a strain is difficult. Several enzymes, such as peroxidase, laccase and lignin peroxidise, can also be used for lignin valorisation and be used to produce various compounds of pharmaceutical value, such as biopolymers, reuseable adsorbents, resins and biodiesels. This chapter deals mainly with various biotechnological approaches that use various microbes and enzymes for lignin valorisation in cost-effective and ecofriendly ways
Study of Lithofacies, Radar Facies and Satellite Images of the River Bars: Implication for Regulation Structures on the Tista River of Eastern Himalaya
No full textThe study examines the sedimentary facies of the Tista River bars in the foothill alluvial plain of the Eastern Himalaya. We have used, following our earlier work, a scheme of classification of the river bars in pre-dam (TOL) and post-dam (TNL) category based on the study of the last 31 years’ satellite images. The present study explores if the pre-dam bars differ in their sedimentary facies and overall architecture from that of the post-dam bars. For this purpose, we studied the cutbank sections of selected pre-dam and post-dam bars and conducted Ground Penetrating Radar (GPR) survey of the bars of these two categories. We defined eight facies and four facies associations from the surface exposures of the Tista bars and categorized the GPR signals into eight radar facies. The study reveals contrasting patterns of structure and architecture of the pre-dam and post-dam bars. The typical bar building architecture (like upstream or downstream accreting surfaces, bar top channel incision, fining and thinning upward succession) and evidence of remarkable flow fluctuation have been recorded in the TOL bars. In contrast, the TNL bars appear to be entirely different with monotonously aggrading cosets of trough cross strata forming sheet-like units as the sole feature of these bars. We interpret the internal features of TNL bars and their architecture to be the product of controlled artificial discharge from upstream dams and barrage. The grain size comparison of these two temporal categories of bars also demonstrates fining of grain size in the TNL bars. Recognition of these kinds of contrasting bar architecture allows recognition of the effect of the engineering structures in the natural streams and provides means to assess the hydrological changes during the post-dam period
Entrepreneurship for Inclusive Growth and Sustainable Development
No full textThis National Rural Entrepreneurship Summit (NRES-2024) report was edited by Dr. Hari Charan Behera and others.
The Convener of the NRES-2024, Dr Hari Charan Behera, plans to publish a volume based on contributions from selected participants, eminent speakers of the Summit and other scholars to further disseminate ideas and foster knowledge production.https://digitalcommons.isical.ac.in/monographs/1004/thumbnail.jp
On the Tightness Gap Analysis of Reductions of some Lattice problems to the Learning with Error problem
No full textLattice-based cryptography is a highly regarded contender for post-quantum standard- ization by NIST. NIST has already chosen “CRYSTALS-KYBER” a lattice-based public-key encryption and key-establishment algorithm and “CRYSTALS-DILITHIUM”, a lattice-based digital signature algorithm. The current lattice-based schemes are based on Oded Regev’s original construction, which sparked significant interest in the cryptographic community due to its post-quantum security and the equivalence between worst-case and average-case hardness. Oded Regev’s cryptographic scheme is built upon a problem called “Learning with Error” (LWE), which is a generalization of the “Learning Parity with Noise” (LPN) problem. This scheme is straightforward to implement and has gained attention for its simplicity. Previ- ously, Mikolas Ajtai demonstrated the worst-case to average-case equivalence for a set of hard lattice problems. Regev’s seminal paper demonstrated that the security of LWE-based cryptosystems could rely on the hardness of worst-case lattice problems. While this result is theoretically groundbreaking, the reduction from hard lattice problems to LWE is not tightly bound, which limits its practical applicability. The tightness of a reduction is a critical factor often underestimated. The tightness gap of a reduction quantifies the concreteness of the reduction, and a tight reduction is valuable for translating theoretical hardness guarantees into practical scenarios. Cryptography, as a field, prioritizes practical applicability. Non-tight reductions lead to less efficient systems but they have practical applications. Regev’s work has prompted numerous follow-up studies. One significant effort aimed to make the reduction classical, as the original reduction was quantum-based. Additionally, subsequent research has focused on enhancing the efficiency of LWE-based cryptosystems by utilizing various algebraic variants of lattices, such as ideal and module lattices. We thoroughly investigate these major reductions to unveil their true significance in terms of reduction tightness. Furthermore, we conduct a concrete security analysis of these reductions and identify several areas for improvement
Variants of vertex and edge colorings of graphs
No full textA k-linear coloring of a graph G is an edge coloring of G with k colors so that each color class forms a linear forest—a forest whose each connected component is a path. The linear arboricity χ′ l(G) of G is the minimum integer k such that there exists a k-linear coloring of G. Akiyama, Exoo and Harary conjectured in 1980 that for every graph G, χ′ l(G) ≤ l∆(G)+1 2 m where ∆(G) is the maximum degree of G. First, we prove the conjecture for 3-degenerate graphs. This establishes the conjecture for graphs of treewidth at most 3 and provides an alternative proof for the conjecture in some classes of graphs like cubic graphs and triangle-free planar graphs for which the conjecture was already known to be true. Next, we prove that for every 2-degenerate graph G, χ′ l(G) = l∆(G) 2 m if ∆(G) ≥ 5. We conjecture that this equality holds also when ∆(G) ∈ {3, 4} and show that this is the case for some well-known subclasses of 2-degenerate graphs. All the above proofs can be converted into linear time algorithms that produce linear colorings of input 3-degenerate and 2-degenerate graphs using a number of colors matching the upper bounds on linear arboricity proven for these classes of graphs. Motivated by this, we then show that for every 3-degenerate graph, χ′ l(G) = l∆(G) 2 m if ∆(G) ≥ 9. Further, we show that this line of reasoning can be extended to obtain a different proof for the linear arboricity conjecture for all 3-degenerate graphs. This proof has the advantage that it gives rise to a simpler linear time algorithm for obtaining a linear coloring of an input 3-degenerate graph G using at most one more color than the linear arboricity of G. A p-centered coloring of a graph G, where p is a positive integer, is a coloring of the vertices of G in such a way that every connected subgraph of G either contains a vertex with a unique color or contains more than p different colors. As p increases, we get a hierarchy of more and more restricted colorings, starting from proper vertex colorings, which are exactly the 1-centered colorings. Debski, Felsner, Micek and Schroder proved that bounded degree graphs have p-centered colorings using O(p) colors. But since their method is based on the technique of entropy compression, it cannot be used to obtain a description of an explicit coloring even for relatively simple graphs. In fact, they ask if an explicit p-centered coloring using O(p) colors can be constructed for the planar grid. We answer their question by demonstrating a construction for obtaining such a coloring for the planar grid
A Bayesian quantile joint modeling of multivariate longitudinal and time-to-event data
No full textLinear mixed models are traditionally used for jointly modeling (multivariate) longitudinal outcomes and event-time(s). However, when the outcomes are non-Gaussian a quantile regression model is more appropriate. In addition, in the presence of some time-varying covariates, it might be of interest to see how the effects of different covariates vary from one quantile level (of outcomes) to the other, and consequently how the event-time changes across different quantiles. For such analyses linear quantile mixed models can be used, and an efficient computational algorithm can be developed. We analyze a dataset from the Acute Lymphocytic Leukemia (ALL) maintenance study conducted by Tata Medical Center, Kolkata. In this study, the patients suffering from ALL were treated with two standard drugs (6MP and MTx) for the first two years, and three biomarkers (e.g. lymphocyte count, neutrophil count and platelet count) were longitudinally measured. After treatment the patients were followed nearly for the next three years, and the relapse-time (if any) for each patient was recorded. For this dataset we develop a Bayesian quantile joint model for the three longitudinal biomarkers and time-to-relapse. We consider an Asymmetric Laplace Distribution (ALD) for each outcome, and exploit the mixture representation of the ALD for developing a Gibbs sampler algorithm to estimate the regression coefficients. Our proposed model allows different quantile levels for different biomarkers, but still simultaneously estimates the regression coefficients corresponding to a particular quantile combination. We infer that a higher lymphocyte count accelerates the chance of a relapse while a higher neutrophil count and a higher platelet count (jointly) reduce it. Also, we infer that across (almost) all quantiles 6MP reduces the lymphocyte count, while MTx increases the neutrophil count. Simulation studies are performed to assess the effectiveness of the proposed approach
A one-chart scheme for joint monitoring of the two parameters of zero-inflated Poisson processes
No full textThe high-quality processes usually have more count of zeros than are expected under chance variation and are commonly modeled by zero-inflated Poisson (ZIP) distribution. A ZIP model has two parameters— (Formula presented.) ((Formula presented.)) and (Formula presented.) ((Formula presented.) \u3e0). Often separate control charts are used for monitoring the two parameters. But a one-chart scheme for joint monitoring of the two parameters offers significant operational advantages. A few one-chart schemes for joint monitoring of the two parameters are reported in literature. However, the monitoring statistic of none of these schemes is defined directly on the observed quality characteristics. This leads to difficulty in understanding of these schemes by the practitioners. Any control charting scheme developed directly on the observed quality characteristic is intuitively appealing to the practitioners and can be easy to interpretations by the practitioners. In this article, a one-chart scheme (called Gamma chart) is developed considering average number of nonconformities in the samples as the monitoring statistic. The performance of the Gamma chart is studied via simulation. The results reveal that it efficiently detect the out-of-control process conditions resulting from moderate shifts in (Formula presented.) and/or (Formula presented.) Finally, a case study from an Indian automobile industry is presented
A Scalable t-Wise Coverage Estimator: Algorithms and Applications
No full textOwing to the pervasiveness of software in our modern lives, software systems have evolved to be highly configurable. Combinatorial testing has emerged as a dominant paradigm for testing highly configurable systems. Often constraints are employed to define the environments where a given system is expected to work. Therefore, there has been a sustained interest in designing constraint-based test suite generation techniques. A significant goal of test suite generation techniques is to achieve t-wise coverage for higher values of t. Therefore, designing scalable techniques that can estimate t-wise coverage for a given set of tests and/or the estimation of maximum achievable t-wise coverage under a given set of constraints is of crucial importance. The existing estimation techniques face significant scalability hurdles. We designed scalable algorithms with mathematical guarantees to estimate (i) t-wise coverage for a given set of tests, and (ii) maximum t-wise coverage for a given set of constraints. In particular, ApproxCov takes in a test set U and returns an estimate of the t-wise coverage of U that is guaranteed to be within (1±ϵ)-factor of the ground truth with probability at least 1-δ for a given tolerance parameter ϵ and a confidence parameter δ. A scalable framework ApproxMaxCov for a given formula F outputs an approximation which is guaranteed to be within (1±ϵ) factor of the maximum achievable t-wise coverage under F, with probability ≥1-δ for a given tolerance parameter ϵ and a confidence parameter δ. Our comprehensive evaluation demonstrates that ApproxCov and ApproxMaxCov can handle benchmarks that are beyond the reach of current state-of-the-art approaches. In this paper we present proofs of correctness of ApproxCov, ApproxMaxCov, and of their generalizations. We show how the algorithms can improve the scalability of a test suite generator while maintaining its effectiveness. In addition, we compare several test suite generators on different feature combination sizes t
Alteration of Rice Root Endophytic Bacterial Community Composition by Meloidogyne graminicola and Identification of Potential Biocontrol Agent
No full textIntroduction: Rice root gall is a severe infection caused by the rice root-knot nematode Meloidogyne graminicola. Overuse of chemical nematicides intensifies the need for a suitable biocontrol agent. Nematode infestation in plants alters the associated microbiome; however, their correlations need to be better understood. Hence, this work aimed to unravel the changes in indigenous endophytic bacterial community composition of rice root because of infection caused by M. graminicola and also to identify dominant bacteria strains as a potential biological control agent. Material & Methods: The endophytic bacterial community of non-infected rice root and gall was analysed using a 16 S rRNA gene-based metagenomics approach. The dominant endophytic bacterial community was further isolated and screened for its PGP and nematicidal activity using bacterial cell suspension and culture filtrate to identify a potential biocontrol agent. Result and Discussion: Our results show that nematode infection has altered the bacterial community composition, and a distinct community existed between gall and non-infected roots. This shift in the microbial community is associated with reduced species richness due to infection. We also observed that a few endophytic genera like Chryseobacterium, Rhizobium, Gemmata, and Pseudomonas that were unique to gall are reported to have been associated either with nematode or may have been recruited by plants as a growth promoter to combat nematode infection. Other bacterial endophytes that are specific to the non-infected root microbiome, like Delftia, Bacillus, Pantoea, Acidovorax, and Azorhizobium, are hypothesised to remain associated with rice seeds, and they possess biological control/plant growth promotion abilities. Further, after screening all isolates, Enterobacter sp. strain SSNI 8 isolated from a non-infected root was evaluated for its efficiency in acting as a nematicidal agent against M. graminicola, and we found that the strain showed 90% nematode mortality with its culture filtrate which may possess some secondary metabolites antagonistic to the nematode. Conclusion: Overall, this study provided a comprehensive view of endophytes associated with gall in non-infected roots and identified a potential biocontrol agent
Appraisal of pollution and health risks associated with coal mine contaminated soil using multimodal statistical and Fuzzy-TOPSIS approaches
No full textThe present study assesses the concentration, probabilistic risk, source classification, and dietary risk arising from heavy metal (HMs) pollution in agricultural soils affected by coal mining in eastern part of India. Analyses of soil and rice plant indicated significantly elevated levels of HMs beyond the permissible limit in the contaminated zones (zone 1: PbSoil: 108.24 ± 72.97, CuSoil: 57.26 ± 23.91, CdSoil: 8.44 ± 2.76, CrSoil: 180.05 ± 46.90, NiSoil: 70.79 ± 25.06 mg/kg; PbGrain: 0.96 ± 0.8, CuGrain: 8.6 ± 5.1, CdGrain: 0.65 ± 0.42, CrGrain: 4.78 ± 1.89, NiGrain: 11.74 ± 4.38 mg/kg. zone 2: PbSoil: 139.56 ± 69.46, CuSoil: 69.89 ± 19.86, CdSoil: 8.95 ± 2.57, CrSoil: 245.46 ± 70.66, NiSoil: 95.46 ± 22.89 mg/kg; PbGrain: 1.27 ± 0.84, CuGrain: 7.9 ± 4.57, CdGrain: 0.76 ± 0.43, CrGrain: 8.6 ± 1.58, NiGrain: 11.50 ± 2.46 mg/kg) compared to the uncontaminated zone (zone 3). Carcinogenic and non-carcinogenic health risks were computed based on the HMs concentration in the soil and rice grain, with Pb, Cr, and Ni identified as posing a high risk to human health. Monte Carlo simulation, the solubility-free ion activity model (FIAM), and severity adjusted margin of exposure (SAMOE) were employed to predict health risk. FIAM hazard quotient (HQ) values for Ni, Cr, Cd, and Pb were \u3e 1, indicating a significant non-carcinogenic risk. SAMOE (risk thermometer) results for contaminated zones ranged from low to moderate risk (CrSAMOE: 0.05, and NiSAMOE: 0.03). Fuzzy-TOPSIS and variable importance plots (from random forest) showed that Ni and Cr were mostly responsible for the toxicity in the rice plant, respectively. A self-organizing map for source classification revealed common origin for the studied HMs with zone 2 exhibiting the highest contamination. The positive matrix factorization model for the source apportionment identified coal mining and transportation as the predominant sources of HMs. Spatial distribution analysis indicated higher contamination near mining sites as compared to distant sampling sites. Consequently, this study will aid environmental scientists and policymakers controlling HM pollution in agricultural soils near coal mines. (Figure presented.