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Background Watermelon juice has gained a lot of consumer interest in recent years due to its richness in phytochemical lycopene and its high antioxidant activity. The water activity of this juice is very high and it has a low acid content. Therefore the microbial activity in watermelon juice is comparatively high than most juices. Due to high percentage of spoilage microorganisms, it is quite difficult to store it for longer times without processing. Heat treatment has been used a pasteurization technique but it has a negative effect on its nutritional and sensory properties as bioactives are sensitive to high temperature processing. So the need arises for non-thermal processes to treat watermelon juice. Scope and approach In recent years non-thermal or novel thermal processes have been successfully tested in research laboratories for processing of watermelon juice. These processes include pulsed electric field (PEF), ultraviolet irradiation (UV), sonication, ohmic heating (OH), high pressure processing (HHP), high pressure carbon dioxide (HPCD), nano fluid thermal processing (NFT) and membrane technology. The effects of these processes on micorobial activity and physicochemical properties of watermelon juice have been reported in this study. Key findings and conclusions Non-thermal processes have been quite successfully analyzed for processing watermelon juice. They showed positive results in reducing the microbial spoilage of the juice and at the same time have retained a high portion of nutritional compounds. Among the non-thermal processes, PEF is the most widely used for watermelon juice processing and also showed most acceptable results.

A Keggin silicotungstic acid (HPS) catalyst was heterogenized by loading (10–40 wt %) over zirconia support, and the resulting catalysts were named ESZN-1 (10 wt %), ESZN-2 (20 wt %), ESZN-3 (30 wt...

The advent of Forward Osmosis (FO) has ushered in a new revolution in the field of membrane technology. Besides the oft-studied application of water purification, FO has opened up new frontiers of research in diverse fields. All of them are potentially sustainable technology solutions characterized by a high degree of process intensification. It is interesting to imagine different avenues where the potent combination of membrane technology and osmotic gradient can be used. This brief review evaluates four such emerging applications of FO; viz: Membrane Crystallization, Osmotic Pumps, Energy Production, and Membrane Evaporation which will be considered as the most important in the near future. All four aforesaid applications were critically reviewed with respect to the principle of operation, process design fundamentals, cost distribution, tangible benefits over conventional alternatives and commercial ventures around the world. Comparative assessments of transport flux, yield, configuration, and membrane module was carried out. Several issues relating to performance, hydrodynamics and scale up designs were analyzed with special emphasis on concentration polarization, flux decline, nature of the membrane.

The paper investigates increasing permeate flux (%) of the disc membrane which can improve the quality of rubber industrial effluent of Tripura. Response surface methodology was used to optimize the independent influencing parameters to improve the permeate flux. The effect of different influencing parameters like operating pressure, membrane pore size, and inlet feed velocity on membrane permeate flux were studied to determine the optimum operating conditions within the predefined boundary. The experiments were pre-planned and designed according to central composite rotatable design, and second-order polynomial regression model was developed for regression and analysis of variance study. Results show the membrane has maximum permeate flux (%) when the operating pressure is 14.50 Pa, pore size is 0.20 μm, and inlet feed velocity is 2.10 m/s. The Pareto analysis in the study established that the inlet velocity was the most influential parameter in the model equation.

The main problems of membrane-based separation methods especially pressure driven ones are fouling and concentration polarization. After increasing cross-flow velocity, use of promoters for inducin...

Molecular tweezers (MTs) are broad-spectrum inhibitors of abnormal protein aggregation. A lead MT, called CLR01, has been demonstrated to inhibit the aggregation and toxicity of multiple amyloidogenic proteins in vitro and in vivo. Previously, we evaluated the effect of CLR01 in the 3 × Tg mouse model of Alzheimer's disease, which overexpresses mutant human presenilin 1, amyloid β-protein precursor, and tau and found that subcutaneous administration of the compound for 1 month led to a robust reduction of amyloid plaques, neurofibrillary tangles, and microgliosis. CLR01 also has been demonstrated to inhibit tau aggregation in vitro and tau seeding in cell culture, yet because in Alzheimer's disease (AD) and in the 3 × Tg model, tau hyperphosphorylation and aggregation are thought to be downstream of Aβ insults, the study in this model left open the question whether CLR01 affected tau in vivo directly or indirectly.To determine if CLR01 could ameliorate tau pathology directly in vivo, we tested the compound similarly using the P301S-tau (line PS19) mouse model. Mice were administered 0.3 or 1.0 mg/kg per day CLR01 and tested for muscle strength and behavioral deficits, including anxiety- and disinhibition-like behavior. Their brains then were analyzed by immunohistochemical and biochemical assays for pathological forms of tau, neurodegeneration, and glial pathology.CLR01 treatment ameliorated muscle-strength deterioration, anxiety-, and disinhibition-like behavior. Improved phenotype was associated with decreased levels of pathologic tau forms, suggesting that CLR01 exerts a direct effect on tau in vivo. Limitations of the study included a relatively short treatment period of the mice at an age in which full pathology is not yet developed. In addition, high variability in this model lowered the statistical significance of the findings of some outcome measures.The findings suggest that CLR01 is a particularly attractive candidate for the treatment of AD because it targets simultaneously the two major pathogenic proteins instigating and propagating the disease, amyloid β-protein (Aβ), and tau, respectively. In addition, our study suggests that CLR01 can be used for the treatment of other tauopathies in the absence of amyloid pathology.

Photocatalytic methods for hydrogen generation and organic pollutant degradation can be considered as economic, and environment-friendly techniques. Transition metal oxides are most commonly used in photo-assisted processes and exhibit enhanced photocatalytic response in hydrogen generation and hazardous contaminant degradation. Various pollutants can be utilized as sacrificial agents according to the available literature. Instead of performing the processes separately, our intention in this chapter is to review various aspects of simultaneous hydrogen generation and wastewater purification. Thus two significant areas of concern, namely the future energy crisis and wastewater purification can be solved with reduced operating cost and time. Coupled photocatalysis for enhanced hydrogen evolution and efficient pollutant decontamination is drawing the attention of researchers worldwide.

Here we revisit the quantum algorithms for obtaining Forrelation [Aaronson et al, 2015] values to evaluate some of the well-known cryptographically significant spectra of Boolean functions, namely the Walsh spectrum, the cross-correlation spectrum and the autocorrelation spectrum. We introduce the existing 2-fold Forrelation formulation with bent duality based promise problems as desirable instantiations. Next we concentrate on the $3$-fold version through two approaches. First, we judiciously set-up some of the functions in $3$-fold Forrelation, so that given an oracle access, one can sample from the Walsh Spectrum of $f$. Using this, we obtain improved results than what we obtain from the Deutsch-Jozsa algorithm, and in turn it has implications in resiliency checking. Furthermore, we use similar idea to obtain a technique in estimating the cross-correlation (and thus autocorrelation) value at any point, improving upon the existing algorithms. Finally, we tweak the quantum algorithm with superposition of linear functions to obtain a cross-correlation sampling technique. To the best of our knowledge, this is the first cross-correlation sampling algorithm with constant query complexity. This also provides a strategy to check if two functions are uncorrelated of degree $m$. We further modify this using Dicke states so that the time complexity reduces, particularly for constant values of $m$., Comment: 16 Pages, 5 figures

This chapter explores the concept of visible light active nanocomposites for the enhanced photocatalytic hydrogen generation and dye degradation. Since the late 1960s, A. Fujishima has been involved in unfolding the fascinating characteristics of titanium dioxide (TiO2) as semiconductor oxide. The increased growth in population and industrial development has tremendously increased the generation of waste products and consumption of energy worldwide. This situation creates an immense need of clean and sustainable alternative sources of energy. Hydrogen, having a high energy capacity, is considered as a reliable fuel for the future energy requirements. In addition to that, due to the rapid industrialisation, our water is being contaminated with various harmful industrial effluents. This chapter illustrates the significance of visible light nanocomposites for the photocatalytic application of hydrogen generation for future energy security and dye degradation for the effective effluent treatment of textile industries.

The diagnosis of Parkinson’s disease (PD) and atypical parkinsonian syndromes is difficult due to the lack of reliable, easily accessible biomarkers. Multiple system atrophy (MSA) is a synucleinopathy whose symptoms often overlap with PD. Exosomes isolated from blood by immunoprecipitation using CNS markers provide a window into the brain’s biochemistry and may assist in distinguishing between PD and MSA. Thus, we asked whether α-synuclein (α-syn) in such exosomes could distinguish among healthy individuals, patients with PD, and patients with MSA. We isolated exosomes from the serum or plasma of these three groups by immunoprecipitation using neuronal and oligodendroglial markers in two independent cohorts and measured α-syn in these exosomes using an electrochemiluminescence ELISA. In both cohorts, α-syn concentrations were significantly lower in the control group and significantly higher in the MSA group compared to the PD group. The ratio between α-syn concentrations in putative oligodendroglial exosomes compared to putative neuronal exosomes was a particularly sensitive biomarker for distinguishing between PD and MSA. Combining this ratio with the α-syn concentration itself and the total exosome concentration, a multinomial logistic model trained on the discovery cohort separated PD from MSA with an AUC = 0.902, corresponding to 89.8% sensitivity and 86.0% specificity when applied to the independent validation cohort. The data demonstrate that a minimally invasive blood test measuring α-syn in blood exosomes immunoprecipitated using CNS markers can distinguish between patients with PD and patients with MSA with high sensitivity and specificity. Future optimization and validation of the data by other groups would allow this strategy to become a viable diagnostic test for synucleinopathies.

Background Molecular tweezers (MTs) are broad-spectrum inhibitors of abnormal protein aggregation. A lead MT, called CLR01, has been demonstrated to inhibit the aggregation and toxicity of multiple amyloidogenic proteins in vitro and in vivo. Previously, we evaluated the effect of CLR01 in the 3 × Tg mouse model of Alzheimer’s disease, which overexpresses mutant human presenilin 1, amyloid β-protein precursor, and tau and found that subcutaneous administration of the compound for 1 month led to a robust reduction of amyloid plaques, neurofibrillary tangles, and microgliosis. CLR01 also has been demonstrated to inhibit tau aggregation in vitro and tau seeding in cell culture, yet because in Alzheimer’s disease (AD) and in the 3 × Tg model, tau hyperphosphorylation and aggregation are thought to be downstream of Aβ insults, the study in this model left open the question whether CLR01 affected tau in vivo directly or indirectly. Methods To determine if CLR01 could ameliorate tau pathology directly in vivo, we tested the compound similarly using the P301S-tau (line PS19) mouse model. Mice were administered 0.3 or 1.0 mg/kg per day CLR01 and tested for muscle strength and behavioral deficits, including anxiety- and disinhibition-like behavior. Their brains then were analyzed by immunohistochemical and biochemical assays for pathological forms of tau, neurodegeneration, and glial pathology. Results CLR01 treatment ameliorated muscle-strength deterioration, anxiety-, and disinhibition-like behavior. Improved phenotype was associated with decreased levels of pathologic tau forms, suggesting that CLR01 exerts a direct effect on tau in vivo. Limitations of the study included a relatively short treatment period of the mice at an age in which full pathology is not yet developed. In addition, high variability in this model lowered the statistical significance of the findings of some outcome measures. Conclusions The findings suggest that CLR01 is a particularly attractive candidate for the treatment of AD because it targets simultaneously the two major pathogenic proteins instigating and propagating the disease, amyloid β-protein (Aβ), and tau, respectively. In addition, our study suggests that CLR01 can be used for the treatment of other tauopathies in the absence of amyloid pathology.

A modified SDS–Trizol method was optimized for isolation of total RNA from the stored maize seeds at regular interval of one month for 4 months. Use of SDS extraction buffer before the use of Trizol reduced the co-precipitation problem associated with high carbohydrate content in the seed. Recorded mean RNA yield from seeds across the storage intervals was 978.6 ± 65.46 ng/µl. Average spectrophotometric values (A(260/280)) of isolated RNA varied from 1.974 ± 0.033 to 1.998 ± 0.022. Attempts to isolate RNA from green leaves using Trizol method also ensured comparable quality and quantity of the isolated RNA. RNA yield from fresh leaves was recorded 1008.2 ± 77.088 ng/µl which is slightly higher than the mean RNA yield from seeds across months. Observed mean A(260/280) values of isolated RNA were 1.984 ± 0.030. DNase treatment further improved the A(260/280) ratio in both seeds (2.003 ± 0.006) and leaves (2.012 ± 0.037). High quality and quantity along with integrity of the isolated RNA was ensured through downstream analysis after RNA extraction such as first-strand cDNA synthesis and normal PCR. Extraction of RNA from the stored seeds using modified SDS-based Trizol method and from fresh leaves using Trizol method opened new possibility of understanding role of key genes involving developmental steps especially in the stored seeds.

We develop a framework to study the mechanical response of athermal amorphous solids via a coupling of mesoscale and microscopic models. Using measurements of coarse-grained quantities from simulations of dense disordered particulate systems, we present a coherent elastoplastic model approach for deformation and flow of yield stress materials. For a given set of parameters, this model allows us to match consistently transient and steady state features of driven disordered systems with diverse preparation histories under both applied shear-rate and creep protocols.

Heterogeneity in dynamics in the form of non-Gaussian molecular displacement distributions appears ubiquitously in soft matter. We address the quantification of such heterogeneity using an information-theoretic measure of the distance between the actual displacement distribution and its nearest Gaussian estimation. We explore the usefulness of this measure in two generic scenarios of random walkers in heterogeneous media. We show that our proposed measure leads to a better quantification of non-Gaussianity than the conventional ones based on moment ratios.

Here we study the characteristic length scales in an aqueous suspension of a symmetric oppositely charged colloid subjected to a uniform electric field by Brownian dynamics simulations. We consider the in-plane structure in the presence of a sufficiently strong electric field where the like charges in the system form macroscopic lanes. We construct spatial correlation functions characterizing the structural order and that of particles of different mobilities in the plane transverse to the electric field at a given time. We call these functions equal time density correlation functions (ETDCFs). The ETDCFs between particles of different charges, irrespective of mobilities, are the structural ETDCFs, while those between particles of different mobilities are the dynamic ETDCFs. We extract the characteristic length of correlation by fitting the envelopes of the ETDCFs to exponential dependences. We find that the correlation length scales of the structural ETDCFs and the dynamic ETDCFs of the slow particles increase with time in a concurrent manner. This suggests that the clustering of particles tends to build up dynamically correlated slow particles in the plane transverse to the lanes. The ETDCFs can be measured for colloidal systems by directly following the particle motion by video-microscopy and may be useful to understand the patterns out of equilibrium.

As we step into the era of another Industrial Revolution, the mode of transportation also demands a paradigm shift, especially in a country like India. Pollution, the forerunner of challenges in any Indian city can be substantially dealt with by the introduction of Electric Vehicles or EV.

Uninterrupted supply of adequate and clean drinking water is a fundamental human necessity, and essential to maintain the healthy surroundings. It also influences the development and economic growth, specifically in developing countries. It provides an excellent opportunity for treatment of surface water, groundwater, and wastewater contaminated by toxic metal ions, organic and inorganic solutes, and microorganisms. It facilitates an opportunity to optimize the current conventional techniques and to offer novel and innovative methods of water purification. Various nanomaterials on which purification of contaminated drinking water is based includes nanostructured catalytic membranes, nanosorbents, nanocatalysts, and bioactive nanoparticles. Current and potential applications of nanotechnology in water purification are adsorption, membrane separation processes, photocatalysis, disinfection, and microbial control. This chapter reviews the recent research efforts accomplished in the area of nanotechnology for drinking water purification.

Photocatalytic water-splitting technology by using nano-sized TiO2 can produce low cost and environment-friendly hydrogen using renewable resource such as solar energy, which can fulfil the future requirements of energy. Nano-sized TiO2 is highly efficient as a semiconductor photocatalyst possessing high surface area. The significant drawbacks of utilising TiO2 as photocatalysts are reduced absorption capacity of visible radiation and fast recombination of photoexcited electron/hole (e−/h+) pair. Its activity is restricted to UV light, which is only ∼3–5% of the solar spectrum. This present review elucidates various aspects and the recent researches related to TiO2 nano photocatalysis for the effective solar hydrogen generation via photocatalytic water splitting technology.

Objective In this paper, we proposed a new feature extraction approach based on the multivariate auto regressive model (MVAR) model of the sensitive intrinsic mode function (IMF) groups in the multivariate empirical mode decomposition (MEMD) domain. Approach We computed eigen values from the coefficient matrix of the MVAR model for classifying three different non-motor cognitive task in EEG based brain computer interface (BCI) system. In the first stage, the application of MEMD to multichannel EEG data gave rise to adaptive i.e. data driven decomposition of the multivariate time series data into a large number of IMF groups. In the second stage, the sensitive IMF groups were selected according to their task correlation factor. MVAR model of order six was developed from the five sensitive IMF groups and finally the eigen values of the correlation matrix derived from the coefficient matrix was employed for forming the feature vectors. At the last stage, the extracted feature vectors were fed to a Least Squares Support Vector Machine (LS-SVM) classifier for automatic classification of mental task EEG signals. We tested our approach on the mental task EEG data sets of three subjects. Main result We achieved highest value of average classification accuracy of 94.43% for binary classification of the first pair of mental task i.e baseline and mental arithmetic task using polynomial kernel and 91.65% for the second pair i.e mental arithmetic and mental letter composing task using radial basis function (RBF) with ten fold cross validation. We achieved highest value of average classification accuracy of 77.77% for three class classification employing One Vs One scheme of multiclass SVM classifier. Significance The performance of the binary classifier was evaluated on various parameters such as accuracy, specificity and sensitivity. The encouraging results show the potential of the proposed approach for classifying any non linear and non-stationary signals.

The structural, morphological, and optical properties of the sol–gel derived TiO2 nanoparticles at different pH and calcination temperature were investigated in the present study. X-ray diffraction (XRD), Thermogravimetric analysis (TGA), Field emission scanning electron microscopy (FE-SEM), Transmission electron microscopy (TEM), UV–Visible(Vis) spectroscopy, energy dispersive studies (EDS), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray photoluminescence (PL) spectroscopy, BET surface area analysis, and Barrett-Joyner-Halenda (BJH) pore size distribution and pore volume analysis were used to characterize the prepared TiO2 photocatalyst. The range of crystallite size and band gap of the synthesized TiO2 samples were in the range of 20–80 nm and 2.5–3.2 eV respectively. The photocatalytic performance of prepared TiO2 photocatalysts was evaluated by photodegradation of Methylene Blue (MB) solution under simulated solar irradiation. Results illustrate that the synthesized TiO2 exhibits visible light activity at higher calcination temperature. Crystallinity and surface area plays a vital role in the overall performance of the prepared TiO2 photocatalyst.

In a model system of oppositely charged colloids we study via Brownian dynamics simulation the dynamical response as the system approaches steady states upon application of a constant electric field. The system is known to form patterns of like charges in the transverse plane to the field that are elongated along the field as lanes. We show that an increase in structural heterogeneity leads to non-Gaussian tails in the probability distribution of particle displacements [self van Hove functions (self-vHfs)]. The self-diffusion coefficient depends upon the time of the observations and consequently indicates aging in the system. However, the anomalies in the self-vHfs and diffusion do not appear during the melting of the structures.

The purpose of this research paper is to present a new framework for EEG feature extraction based on the combination of multivariate empirical mode decomposition (MEMD) and phase space reconstruction (PSR) for classifying a small set of non-motor cognitive task EEG signals in mental task based multitask brain computer interface(BCI) system. Our proposed approach employed phase space analysis of the intrinsic mode functions (IMFs) generated by MEMD based decomposition of the six channels EEG signals. The combination of two powerful signal processing techniques i.e. MEMD with phase space reconstruction (PSR) enabled us to characterize the nonlinear and non-stationary nature of the dynamics underlying a particular cognitive task more accurately. Our proposed approach consists of three stages, in the first stage; the application of MEMD to multichannel EEG data gave rise to adaptive i.e. data driven decomposition of the multivariate time series data into a set of IMF groups. All the member IMFs within a group have common oscillatory frequency but different amplitude and cortical origin. In the second stage, a small subset of IMF groups was selected according to their task correlation factor and subsequently represented in the two dimensional phase space through their trajectory matrices. In the third stage, largest singular values of the trajectory matrices corresponding to a subset of sensitive IMFs were employed for forming the feature vectors. Finally, the extracted feature vectors were fed to a least square support vector machine (LS-SVM) classifier for binary i.e. pair wise classification of these mental task EEG signals. With the new feature vectors, it is shown that LS-SVM with RBF kernel provides accuracy of 83.33% in classifying between mental arithmetic and mental letter composing. The performance of this classifier was evaluated on various parameters such as accuracy, specificity and sensitivity. The classification results show the potential of the proposed approach for classifying any non-linear a non- stationary signal.

TiO2 nanoparticles with excellent crystallinity were synthesized via solvothermal method and supported on the ground nano-sized egg shell waste at different loading concentrations. The photocatalytic degradation of the mixture of Methylene blue (MB) and Rhodamine 6G (Rh 6G) in aqueous solution utilizing prepared TiO2 nano composite under visible light irradiation was analysed. Different characterizations including UV–Visible (UV–Vis) spectroscopy, Field emission scanning electron microscopic (FE-SEM), energy dispersive studies (EDS), Transmission electron microscopy (TEM) were performed to investigate the physicochemical properties of the prepared catalyst. In addition, FE-SEM and EDS analysis of the prepared egg shell support were carried out to confirm the morphology, particle size distribution, and composition. The solvothermal derived TiO2 nanocrystals supported on ground nano-sized egg shell waste exhibited enhanced photocatalytic activity as compared to the as-prepared TiO2 nanoparticles. The synergistic effect of TiO2 nanoparticles and nano egg shell support is attributed to the effective dispersion of TiO2 that offers high specific surface area.

Membrane technology has emerged as a substitute to traditional juice clarification and concentration processes as they require less manpower, reduce operating cost and low temperature. It is a low temperature process in which the organoleptic quality of the juice is almost retained. The advantages of these membrane processes over traditional methods are lower thermal damage to product, increase in aroma retention, less energy consumption, and lower equipment costs. Membrane concentration of fruit juice not only provides microbiological stability but also permits economy in packaging and distribution of the finished product due to a reduction in bulk by weight and volume. The biggest problem in the use of membrane based processes for the clarification/concentration of fruit juices is membrane fouling. Membrane fouling manifests itself as a decline in flux with the time of operation, reducing the membrane permeability. The degree of membrane fouling determines the frequency of cleaning, the lifetime of the membrane, the membrane area needed and consequently costs, design and operation of membrane plants. In this review, different membrane separation methods including microfiltration, ultrafiltration, nanofiltration and reverse osmosis for fruit juice clarification/concentration reported in the literature in the last fifteen years are discussed. Membrane Distillation methods for juice concentration is also covered in this review.

Ethyl levulinate (EL) synthesis from biorenewable feedstocks in the presence of metal salts and under non-microwave instant heating reaction environment has been studied. Initial screening suggested CuCl2 and FeCl3 as an efficient catalyst for EL synthesis from 5-hydroxymethylfurfural (HMF), thereby resulting into 47.5% and 43.3% EL yield respectively at 160 °C in 5 min. Interestingly, environmentally benign and widely available metal salt, AlCl3, was found to yield comparable EL (39.0%) when experiments were performed under identical reaction conditions. Therefore, further study was performed using AlCl3 to estimate the effect of operating parameters and catalyst concentration over EL yield and HMF conversion. Thereafter, a comparative study on the effect of microwave vs non-microwave instant heating reaction environment has been done to develop a sustainable process for EL synthesis. Consequently, mechanistic insights on reactant conversion and product yield have been attempted to understand various pathways followed. Thereafter, effect of reaction temperature, catalyst loading, ethanol concentration and heating rates were explored. EL synthesis from HMF under instant heating reaction conditions in the presence of metal salts is reported for the first time. We anticipate that present work would be helpful in providing insights on effects of instant heating method and operating conditions for EL synthesis from HMF.

Methane generation from waste landfills is one of the biggest contributors to global warming. The purpose of this study was twofold: (i) to investigate methane concentration from Municipal Solid Waste (MSW) at three landfills in Dhanbad city, India and (ii) to evaluate the amount of energy that could be recovered based on the MSW characteristics if it were to be incinerated. The waste samples were collected and analysed for composition, energy content, and methane concentration. Results from MSW characterisation revealed that the main component of Dhanbad MSW is organic waste, which made up to 75% of the waste by weight. Methane concentration and moisture content from Railway station (site 1) and Memco-more (site 2 and site 3) measured as 140.53, 18.18 and 20.28 ppm methane/g waste and 25.49, 3.40 and 2.96% dry weight respectively. The calorific value for the waste samples ranged between 10.7 and 13.0 MJ/kg. These findings confirm that the methane generated at the sites can be used for energy recovery. Additionally, the energy content of the MSW suggests that it is a suitable feedstock that can be utilized for electricity generation through combustion.

Ischemic injury to white matter tracts is increasingly recognized to play a key role in age-related cognitive decline, vascular dementia, and Alzheimer’s disease. Knowledge of the effects of ischemic axonal injury on cortical neurons is limited yet critical to identifying molecular pathways that link neurodegeneration and ischemia. Using a mouse model of subcortical white matter ischemic injury coupled with retrograde neuronal tracing, we employed magnetic affinity cell sorting with fluorescence-activated cell sorting to capture layer-specific cortical neurons and performed RNA-sequencing. With this approach, we identified a role for microtubule reorganization within stroke-injured neurons acting through the regulation of tau. We find that subcortical stroke-injured Layer 5 cortical neurons up-regulate the microtubule affinity-regulating kinase, Mark4, in response to axonal injury. Stroke-induced up-regulation of Mark4 is associated with selective remodeling of the apical dendrite after stroke and the phosphorylation of tau in vivo. In a cell-based tau biosensor assay, Mark4 promotes the aggregation of human tau in vitro. Increased expression of Mark4 after ischemic axonal injury in deep layer cortical neurons provides new evidence for synergism between axonal and neurodegenerative pathologies by priming of tau phosphorylation and aggregation. Electronic supplementary material The online version of this article (10.1186/s40478-019-0783-6) contains supplementary material, which is available to authorized users.

Lignocellulosic biomass has positioned itself as a most dominant candidate to cater growing fuel and chemical demands. Moreover, the production and application of biomass-derived chemicals and fuels could help to reduce greenhouse gases and pollutant emissions that damage the environment. In this context, 5-hydroxymethylfurfural (HMF), 5-ethoxymethylfurfural (EMF), levulinic acid (LA), and ethyl levulinate (EL) have emerged as platform-building block molecules to produce a wide-ranging commodity chemicals and fuels. Interestingly, the production of these chemicals from bio-renewable resources is a challenging task among the optimal operating temperatures, reaction times, reaction mechanisms, heating modes, and other factors. In general, depolymerization, isomerization, dehydration, and rehydration are the major reactions, which essentially require a suitable acid catalyst containing both Bronsted and Lewis acidic sites. An optimum ratio of Bronsted and Lewis acidic sites is the dominant factor in determining the desired reactant conversion and products selectivity. Therefore, the motive of this chapter is to enhance the knowledge about the role of catalysts in production of value-added chemicals from various bio-renewable feedstocks.

Exosomes contain cell- and cell-state-specific cargos of proteins, lipids, and nucleic acids and play significant roles in cell signaling and cell-cell communication. Current research into exosome-based biomarkers has relied largely on analyzing candidate biomarkers, i.e., specific proteins or nucleic acids. However, this approach may miss important biomarkers that are yet to be identified. Alternative approaches are to analyze the entire exosome system, either by "omics" methods or by techniques that provide "fingerprints" of the system without identifying each individual biomolecule component. Here, we describe a platform of the latter type, which is based on surface-enhanced Raman spectroscopy (SERS) in combination with multivariate analysis, and demonstrate the utility of this platform for analyzing exosomes derived from different biological sources. First, we examined whether this analysis could use exosomes isolated from fetal bovine serum using a simple, commercially available isolation kit or necessitates the higher purity achieved by the "gold standard" ultracentrifugation/filtration procedure. Our data demonstrate that the latter method is required for this type of analysis. Having established this requirement, we rigorously analyzed the Raman spectral signature of individual exosomes using a unique, hybrid SERS substrate made of a graphene-covered Au surface containing a quasi-periodic array of pyramids. To examine the source of the Raman signal, we used Raman mapping of low and high spatial resolution combined with morphological identification of exosomes by scanning electron microscopy. Both approaches suggested that the spectra were collected from single exosomes. Finally, we demonstrate for the first time that our platform can distinguish among exosomes from different biological sources based on their Raman signature, a promising approach for developing exosome-based fingerprinting. Our study serves as a solid technological foundation for future exploration of the roles of exosomes in various biological processes and their use as biomarkers for disease diagnosis and treatment monitoring.

Raw watermelon juice was clarified in a laboratory scale flat plate ultrafiltration system incorporating polyethersulphone membranes with a molecular weight cut-off of 50 kDa. The experiments have been carried out over a wide range of trans-membrane pressures (1-3 bar). The effect of ultrafiltration with molecular weight cut-off (MWCO) of 50 kDa PES membrane on permeate properties is reported. The effects of trans-membrane pressure (TMP) on permeate flux and resistances were studied. The flux decay was analyzed through the resistance in series model which showed increase of both reversible and irreversible resistance with trans-membrane pressure, The TSS content of the permeate was found to be almost the same with feed while the ascorbic acid content in the permeate was on the lower side as compared to in feed.

Worldwide waste valorization and recovery of non-utilized components/unreacted compounds/ side streams/ byproducts are being researched upon. These are indispensable for environment protection, process economics and emerging technology development concepts like zero liquid discharge. Existing techniques suffer from problems like secondary contamination, complex design, costly raw materials, and extreme process conditions. Membrane crystallization offers a robust and simple solution for sustainable recovery of valuables from effluent streams. This review was undertaken to critically assess efficacy of membrane crystallization for value-added product recovery integrating the basic principles of process intensification: economics, efficiency, and sustainability. Processes like membrane distillation integrated crystallization, osmotic membrane crystallization, concentrative pressure-driven crystallization were analysed with respect to selective separation, byproduct recovery percentage, and viability. Several approaches on process intensification and comparative performance evaluation were discussed. Integrative technology options combining conventional methods like precipitation, evaporative crystallization as well as standalone versions were explored in detail to ascertain techno-commercial viability. It was concluded that membrane crystallizers provides an efficient and eco-friendly means for value added product recovery from diverse waste streams. This critical review on insights aims to formulate concrete guidelines towards applicability of membrane based crystallization for value added product recovery.