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"Kumar, Ranjeet"
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Sustainable lubrication
\"This book overviews recent advances in the development of lubricants and their usage in different tribological systems, starting from nanoscale contacts up to macroscale assemblies with specific focus on sustainable green lubrication choices including base fluids. Further, it covers advances and optimization of new type of lubrication systems according to their usage in various tribological systems as gears, bearings, micro-electromechanical systems, and production equipment. Furthermore, the few examples and case studies about utilization of synthetic lubricants in bearings, gears, dental and so forth has been included. Features: explores information on the present and future of sustainable lubricants due to its accelerated demands in industries, provides conceptual overview of lubricant application in manufacturing and automobile industries, discusses lubricants used in the micro-electromechanical systems (MEMS), nano-electromechanical systems (NEMS), tribo-systems under extreme conditions and for biomedical applications, and reviews information about various types of additives and their role in lubricants, and their cost effectiveness. This text also includes case studies related to journal-bearing/gear drive systems. Finally, this shortform book is geared towards students and researchers in mechanical engineering, automobile engineering, chemical engineering and chemistry, manufacturing, mechanical, materials and metallurgy\"-- Provided by publisher.
Book
Dark matter induced proton decays
A
bstract
We propose a novel theoretical framework in which proton decay is induced by the dark matter. While proton decay requires violation of the
B
+
L
symmetry, dark matter stability often relies on the presence of an unbroken symmetry. These seemingly distinct phenomena are unified through the global U(1)
B
+
L
symmetry inherent in the Standard Model. Its spontaneous breaking leads to a residual
Z
4
symmetry, which ensures dark matter stability and forbids proton decay at tree level. Consequently, proton decay occurs at the one-loop level, mediated by dark sector particles. The proton lifetime is linked with the dark matter, the heavier dark matter mass enhancing proton stability, and vice versa. The
O
TeV
masses of the mediators remain consistent with current proton lifetime limits, making them accessible to experimental searches. In particular, the leptoquark mediating proton decay, carrying exotic
B
+
L
charges, leads to a distinctive signature in collider searches. By intertwining proton decay, dark matter stability, and collider phenomenology, this framework offers distinctive signatures that can be probed in current and future experiments.
Journal Article
PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence
Background
Protein methylation, a post-translational modification, is crucial in regulating various cellular functions. Arginine methylation is required to understand crucial biochemical activities and biological functions, like gene regulation, signal transduction, etc. However, some experimental methods, including Chip–Chip, mass spectrometry, and methylation-specific antibodies, exist for the prediction of methylated proteins. These experimental methods are expensive and tedious. As a result, computational methods based on machine learning play an efficient role in predicting arginine methylation sites.
Results
In this research, a novel method called PRMxAI has been proposed to predict arginine methylation sites. The proposed PRMxAI extract sequence-based features, such as dipeptide composition, physicochemical properties, amino acid composition, and information theory-based features (Arimoto, Havrda-Charvat, Renyi, and Shannon entropy), to represent the protein sequences into numerical format. Various machine learning algorithms are implemented to select the better classifier, such as Decision trees, Naive Bayes, Random Forest, Support vector machines, and K-nearest neighbors. The random forest algorithm is selected as the underlying classifier for the PRMxAI model. The performance of PRMxAI is evaluated by employing 10-fold cross-validation, and it yields 87.17% and 90.40% accuracy on mono-methylarginine and di-methylarginine data sets, respectively. This research also examines the impact of various features on both data sets using explainable artificial intelligence.
Conclusions
The proposed PRMxAI shows the effectiveness of the features for predicting arginine methylation sites. Additionally, the SHapley Additive exPlanation method is used to interpret the predictive mechanism of the proposed model. The results indicate that the proposed PRMxAI model outperforms other state-of-the-art predictors.
Journal Article
Cutting the scotogenic loop: adding flavor to dark matter
A
bstract
We introduce a framework for hybrid neutrino mass generation, wherein scotogenic dark sector particles, including dark matter, are charged non-trivially under the
A
4
flavor symmetry. The spontaneous breaking of the
A
4
group to residual
Z
2
subgroup results in the “cutting” of the radiative loop. As a consequence the neutrinos acquire mass through the hybrid “scoto-seesaw” mass mechanism, combining aspects of both the tree-level seesaw and one-loop scotogenic mechanisms, with the residual
Z
2
subgroup ensuring the stability of the dark matter. The flavor symmetry also leads to several predictions including the normal ordering of neutrino masses and “generalized
μ
−
τ
reflection symmetry” in leptonic mixing. Additionally, it gives testable predictions for neutrinoless double beta decay and a lower limit on the lightest neutrino mass. Finally,
A
4
→
Z
2
breaking also leaves its imprint on the dark sector and ties it with the neutrino masses and mixing. The model allows only scalar dark matter, whose mass has a theoretical upper limit of ≲ 600 GeV, with viable parameter space satisfying all dark matter constraints, available only up to about 80 GeV. Conversely, fermionic dark matter is excluded due to constraints from the neutrino sector. Various aspects of this highly predictive framework can be tested in both current and upcoming neutrino and dark matter experiments.
Journal Article
Meta-analysis of QTLs associated with popping traits in maize (Zea mays L.)
The rising demand for popcorn necessitates improving the popping quality with higher yield of popcorn cultivars. Towards this direction several Quantitative Traits Loci (QTLs) for popping traits have been identified. However, identification of accurate and consistent QTLs across different genetic backgrounds and environments is necessary to effectively utilize the identified QTLs in marker-assisted breeding. In the current study, 99 QTLs related to popping traits reported in 8 different studies were assembled and projected on the reference map \"Genetic 2005\" using BioMercator v4.2 to identify metaQTLs with consistent QTLs. Total ten metaQTLs were identified on chromosome 1 (7 metaQTLs) and 6 (3 metaQTLs) with physical distance ranging between 0.43 and 12.75 Mb, respectively. Four identified metaQTLs, viz ., mQTL1_1, mQTL1_5, mQTL1_7 and mQTL6_2 harboured 5–8 QTL clusters with moderately high R 2 value. The clustered QTLs were from two or more experiments. Based on the expression pattern in endosperm and pericarp tissues, a total of 229 genes were selected. Nineteen of these genes are involved in carbohydrate metabolism. Of the 19 genes specifically involved in carbohydrate metabolism, 11 of them were in these regions, implying the importance of these clustered QTLs. MetaQTL1_1 at bin location 1.01 coincided with the reported QTLs related to various agronomic traits like stalk diameter, tassel length, leaf area and plant height. The identified metaQTLs can be further explored for fine mapping and candidate gene identification, which can be validated by loss or gain of function. Identified metaQTLs can be used for introgression of popping traits towards enhancing the popping ability.
Journal Article
Yield optimization, microbial load analysis, and sensory evaluation of mungbean (Vigna radiata L.), lentil (Lens culinaris subsp. culinaris), and Indian mustard (Brassica juncea L.) microgreens grown under greenhouse conditions
Microgreens have been used for raw consumption and are generally viewed as healthy food. This study aimed to optimize the yield parameters, shelf life, sensory evaluation and characterization of total aerobic bacteria (TAB), yeast and mold (Y&M), Escherichia coli , Salmonella spp., and Listeria spp. incidence in mungbean ( Vigna radiata (L.) Wilczek), lentil ( Lens culinaris Medikus subsp. culinaris ), and Indian mustard ( Brassica juncea (L.) Czern & Coss.) microgreens. In mungbean and lentil, seeding-density of three seed/cm 2 , while in Indian mustard, eight seed/cm 2 were recorded as optimum. The optimal time to harvest mungbean, Indian mustard, and lentil microgreens were found as 7 th , 8 th , and 9 th day after sowing, respectively. Interestingly, seed size was found highly correlated with the overall yield in both mungbeans (r 2 = .73) and lentils (r 2 = .78), whereas no such relationship has been recorded for Indian mustard microgreens. The target pathogenic bacteria such as Salmonella spp. and Listeria spp. were not detected; while TAB, Y&M, Shigella spp., and E . coli were recorded well within the limit to cause any human illness in the studied microgreens. Washing with double distilled water for two minutes has shown some reduction in the overall microbial load of these microgreens. The results provided evidence that microgreens if grown and stored properly, are generally safe for human consumption. This is the first study from India on the safety of mungbean, lentils, and Indian mustard microgreens.
Journal Article
A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images
Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80–85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity.
Journal Article
Comparative RNA-Seq analysis unfolds a complex regulatory network imparting yellow mosaic disease resistance in mungbean Vigna radiata (L.) R. Wilczek
Yellow Mosaic Disease (YMD) in mungbean [ Vigna radiata (L.) R. Wilczek] is one of the most damaging diseases in Asia. In the northern part of India, the YMD is caused by Mungbean Yellow Mosaic India Virus (MYMIV), while in southern India this is caused by Mungbean Yellow Mosaic Virus (MYMV). The molecular mechanism of YMD resistance in mungbean remains largely unknown. In this study, RNA-seq analysis was conducted between a resistant (PMR-1) and a susceptible (Pusa Vishal) mungbean genotype under infected and control conditions to understand the regulatory network operating between mungbean-YMV. Overall, 76.8 million raw reads could be generated in different treatment combinations, while mapping rate per library to the reference genome varied from 86.78% to 93.35%. The resistance to MYMIV showed a very complicated gene network, which begins with the production of general PAMPs (pathogen-associated molecular patterns), then activation of various signaling cascades like kinases, jasmonic acid (JA) and brassinosteroid (BR), and finally the expression of specific genes (like PR-proteins, virus resistance and R-gene proteins) leading to resistance response. The function of WRKY, NAC and MYB transcription factors in imparting the resistance against MYMIV could be established. The string analysis also revealed the role of proteins involved in kinase, viral movement and phytoene synthase activity in imparting YMD resistance. A set of novel stress-related EST-SSRs are also identified from the RNA-Seq data which may be used to find the linked genes/QTLs with the YMD resistance. Also, 11 defence-related transcripts could be validated through quantitative real-time PCR analysis. The identified gene networks have led to an insight about the defence mechanism operating against MYMIV infection in mungbean which will be of immense use to manage the YMD resistance in mungbean.
Journal Article
Pyrolysis of low-value waste sawdust over low-cost catalysts: physicochemical characterization of pyrolytic oil and value-added biochar
The present work deals with an experimental investigation into the generation and characterization of pyrolytic oil and biochar from Sal wood sawdust (SW). The pyrolysis experiment was performed in a semi-batch reactor at 500 oC and 80 oC/min heating rate with CaO, CuO, and Al2O3 catalysts. Further, the pyrolytic oil and biochar were investigated using different analyses, including proximate analysis, elemental analysis, thermal stability, GC-MS, FTIR, field emission scanning electron microscopy, electrical conductivity analysis, higher heating value (HHV), zeta potential analysis, and ash content analysis. Pyrolysis results revealed that compared to thermal pyrolysis (46.02 wt%), the pyrolytic oil yield was improved by catalytic pyrolysis with CaO and CuO (50.02 and 48.23 wt%, respectively). Further, the characterization of pyrolytic oil revealed that the loading of catalysts considerably improved the oil's properties by lowering its viscosity (69.50 to 22 cSt), ash content (0.26 to 0.11 wt%), and oxygen content (28.32 to16.60 %) while raising its acidity (4.2 to 9.6), heating value (25.66 to 36.09 MJ/kg), and carbon content (61.79 to 74.28%). According to the FTIR analysis, the pyrolytic oil contained hydrocarbons, phenols, aromatics, alcohols, and oxygenated compounds. Additionally, the GC-MS analysis showed that catalysts significantly reduced oxygenated fractions, phenols (20.23 to 15.26%), acids (12.23 to 6.56%), and increased hydrocarbons (12 to 16 wt%). Additionally, the results of the biochar analysis demonstrated that SW biochar was appropriate for a range of industrial applications, including in catalysts, supercapacitors, fuel cells, and bio-composite materials.
Journal Article
Flavor imprints on novel low mass dark matter
A
bstract
We present a Majorana scotogenic-like loop framework in which neutrino mass generation and dark matter stability are intrinsically connected to the breaking of the discrete flavor symmetry
A
4
. This breaking leads to the emergence of the scoto-seesaw mechanism and a
Z
2
symmetry. This naturally explains the solar and atmospheric mass-squared differences,
∆
m
sol
2
and
∆
m
atm
2
, while simultaneously ensuring dark matter stability. Our model accommodates normal ordering of neutrino masses, with a generalized
μ
-
τ
reflection symmetry shaping the structure of leptonic mixing and a lower limit on the lightest neutrino mass. Moreover, the model provides predictions for the octant of
θ
23
and a strong correlation between
∆
m
sol
2
and
∆
m
atm
2
. This correlation puts a lower bound on the fermionic DM mass. In contrast, scalar dark matter remains viable over a broad mass spectrum. A notable feature is that the low mass regime (~ 15 GeV onwards) survives owing to the presence of efficient co-annihilation channels, which are typically absent in the Majorana scotogenic scenario. Additionally, the model aligns with current and future limits from lepton flavor violation experiments.
Journal Article