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Increasing evidence from diverse sources during the past several years has indicated that long-term, low level, chronic inflammation mediates several chronic diseases including cancer, arthritis, obesity, diabetes, cardiovascular diseases, and neurological diseases. The inflammatory molecules and transcription factors, adhesion molecules, AP-1, chemokines, C-reactive protein (CRP), cyclooxygenase (COX)-2, interleukins (ILs), 5-lipooxygenase (5-LOX), matrix metalloproteinases (MMPs), nuclear factor (NF)-kB, signal transducer and activator of transcription 3 (STAT3), tumor necrosis factor (TNF), and vascular endothelial growth factor (VEGF) are molecular links between inflammation and chronic diseases. Thus, suppression of inflammatory molecules could be potential strategy for the prevention and therapy of chronic diseases. The currently available drugs against chronic diseases are highly expensive, minimally effective and produce several side effects when taken for long period of time. The focus of this review is to discuss the potential of nutraceuticals derived from \"Mother Nature\" such as apigenin, catechins, curcumin, ellagic acid, emodin, epigallocatechin gallate, escin, fisetin, flavopiridol, genistein, isoliquiritigenin, kaempferol, mangostin, morin, myricetin, naringenin, resveratrol, silymarin, vitexin, and xanthohumol in suppression of these inflammatory pathways. Thus, these nutraceuticals offer potential in preventing or delaying the onset of chronic diseases. We provide evidence for the potential of these nutraceuticals from pre-clinical and clinical studies.

SELEX (systematic evolution of ligands by exponential enrichment) is a process that involves the progressive purification from a combinatorial library of nucleic acid ligands with a high affinity for a particular target by repeated rounds of partitioning and amplification. With the development of aptamer technology over the last decade, various modified SELEX processes have arisen that allow various aptamers to be developed against a wide variety of molecules, irrespective of the target size. In the present review, the separation methods used in such SELEX processes are reviewed.

Extensive research within the last several decades has revealed that the major risk factors for most chronic diseases are infections, obesity, alcohol, tobacco, radiation, environmental pollutants, and diet. It is now well established that these factors induce chronic diseases through induction of inflammation. However, inflammation could be either acute or chronic. Acute inflammation persists for a short duration and is the host defense against infections and allergens, whereas the chronic inflammation persists for a long time and leads to many chronic diseases including cancer, cardiovascular diseases, neurodegenerative diseases, respiratory diseases, etc. Numerous lines of evidence suggest that the aforementioned risk factors induced cancer through chronic inflammation. First, transcription factors NF-κB and STAT3 that regulate expression of inflammatory gene products, have been found to be constitutively active in most cancers; second, chronic inflammation such as pancreatitis, prostatitis, hepatitis etc. leads to cancers; third, activation of NF-κB and STAT3 leads to cancer cell proliferation, survival, invasion, angiogenesis and metastasis; fourth, activation of NF-κB and STAT3 leads to resistance to chemotherapy and radiation, and hypoxia and acidic conditions activate these transcription factors. Therefore, targeting these pathways may provide opportunities for both prevention and treatment of cancer and other chronic diseases. We will discuss in this review the potential of various dietary agents such as spices and its components in the suppression of inflammatory pathways and their roles in the prevention and therapy of cancer and other chronic diseases. In fact, epidemiological studies do indicate that cancer incidence in countries such as India where spices are consumed daily is much lower (94/100,000) than those where spices are not consumed such as United States (318/100,000), suggesting the potential role of spices in cancer prevention.

The thermosalient effect is an extremely rare propensity of certain crystalline solids for self-actuation by elastic deformation or by a ballistic event. Here we present direct evidence for the driving force behind this impressive crystal motility. Crystals of a prototypical thermosalient material, (phenylazophenyl)palladium hexafluoroacetylacetonate, can switch between five crystal structures (α—ε) that are related by four phase transitions including one thermosalient transition (α↔γ). The mechanical effect is driven by a uniaxial negative expansion that is compensated by unusually large positive axial expansion (260 × 10 –6  K –1 ) with volumetric expansion coefficients (≈250 × 10 –6  K –1 ) that are among the highest values reported in molecular solids thus far. The habit plane advances at ~10 4 times the rate observed with non-thermosalient transitions. This rapid expansion of the crystal following the phase switching is the driving force for occurrence of the thermosalient effect. The thermosalient effect is the unusual tendency of some crystals to visibly jump during phase changes. Here, the authors study the multiple phase changes in a prototypic thermosalient material and provide evidence for the factors that drive this self-actuation.

In hardrocks that cover about 20% of the Earth’s surface, it is difficult to locate steady sources for groundwater due to inadequate understanding of the fracture networks. A comprehensive knowledge of fracture distribution at the regional scale is necessary to delineate sustainable aquifers and manage them efficiently. The resistivity maps derived from the airborne electromagnetic (AEM) survey over the Ankasandra watershed in Karnataka, India, reveal sharp and deep zones of low formation resistivity, which indicate groundwater-bearing zones. It is found that some of these zones are hydrogeologically connected through fracture networks resulting in augmented yield. AEM results in combination with an in-depth understanding of the geological structures successfully map these groundwater-saturated fracture networks (or hydrogeological lineaments) that we term as ‘Hydrolins’. As groundwater occurrence is generally associated with lineaments, we analyzed the drilling and geophysical logs from 21 wells within a 380 sq.km area to study the relationships of various lineaments with ‘Hydrolins’, particularly in respect of their groundwater potential. AEM results, though calibrated and correlated with a limited number of well data, revealed a threshold groundwater horizon (TGWH), found to be at 80 m depth for Ankasandra watershed, beyond which a strong correlation exists between the depth of a well and its yield. While the TGWH may differ for different watersheds, the approach presented here can be readily adopted to map sustainable groundwater sources in hardrocks worldwide.

Owing to the unique structural characteristics as well as outstanding physio–chemical and electrical properties, graphene enables significant enhancement with the performance of electrospun nanofibers, leading to the generation of promising applications in electrospun-mediated sensor technologies. Electrospinning is a simple, cost-effective, and versatile technique relying on electrostatic repulsion between the surface charges to continuously synthesize various scalable assemblies from a wide array of raw materials with diameters down to few nanometers. Recently, electrospun nanocomposites have emerged as promising substrates with a great potential for constructing nanoscale biosensors due to their exceptional functional characteristics such as complex pore structures, high surface area, high catalytic and electron transfer, controllable surface conformation and modification, superior electric conductivity and unique mat structure. This review comprehends graphene-based nanomaterials (GNMs) (graphene, graphene oxide (GO), reduced GO and graphene quantum dots) impregnated electrospun polymer composites for the electro-device developments, which bridges the laboratory set-up to the industry. Different techniques in the base polymers (pre-processing methods) and surface modification methods (post-processing methods) to impregnate GNMs within electrospun polymer nanofibers are critically discussed. The performance and the usage as the electrochemical biosensors for the detection of wide range analytes are further elaborated. This overview catches a great interest and inspires various new opportunities across a wide range of disciplines and designs of miniaturized point-of-care devices.

Komagataella phaffii, formerly Pichia pastoris (P. pastoris), is a promising methylotrophic yeast used in industry to produce recombinant protein and valuable metabolites. In this study, a genome-scale metabolic model (GEMs) was reconstructed and used to assess P. pastoris’ metabolic capabilities for the production of S-adenosyl-l-methionine (AdoMet or SAM or SAMe) from individual carbon sources along with the addition of l-methionine. In a model-driven P. pastoris strain, the well-established genome-scale metabolic model iAUKM can be implemented to predict high valuable metabolite production. The model, iAUKM, was created by merging the previously published iMT1026 model and the draught model generated using Raven toolbox from the KEGG database which covered 2309 enzymatic reactions associated with 1033 metabolic genes and 1750 metabolites. The highly curated model was successful in capturing P. pastoris growth on various carbon sources, as well as AdoMet production under various growth conditions. Many overexpression gene targets for increasing AdoMet accumulation in the cell have been predicted for various carbon sources. Inorganic phosphatase (IPP) was one of the predicted overexpression targets as revealed from simulations using iAUKM. When IPP gene was integrated into P. pastoris, we found that AdoMet accumulation increased by 16% and 14% using glucose and glycerol as carbon sources, respectively. Our in silico results shed light on the factors limiting AdoMet production, as well as key pathways for rationalized engineering to increase AdoMet yield.

The exclusive photoproduction of vector mesons provides a unique opportunity to constrain the gluon distribution function within protons and nuclei. Measuring vector mesons of various masses over a wide range of rapidity and as a function of transverse momentum provides important information on the evolution of the gluon distribution within nuclei. A variety of measurements, including the exclusive J/ ψ , ρ , and Υ meson production in pPb (at nucleonnucleon center of mass energies of 5.02 and 8.16 TeV) and PbPb (5.02 TeV) collisions, are presented as a function of squared transverse momentum and the photon-proton center of mass energy. Finally, compilations of these data and previous measurements are compared to various theoretical predictions.

This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.