Neurological disorders as a group are the leading cause of disability worldwide, and their contribution to the overall burden from all health conditions is increasing. Aging of the population, population growth, and the shift from communicable to noncommunicable illness are occurring in many countries and regions, and surveillance of the burden of neurological disorders is required to optimize healthcare planning and resource allocation. Disease occurs in patterns that reflect the underlying causes or major risk factors. For example, stroke, dementia, and Parkinson's disease occur primarily in older individuals, whereas multiple sclerosis and primary headache disorders have their onset at earlier ages. Although the reasons for these age-dependent differences are largely unknown, some possibilities include immunological responses that are initially aggressive in early adulthood and wane with time, altered vascular responses to dietary and environmental stimuli, and a breakdown in the clearance of misfolded proteins. Stroke has emerged as a common disorder across the developed and developing worlds as the lifespan increases and lifestyle patterns become similar to those in the United States. Multiple sclerosis has been a geographically spreading disease with its onset in northern Europe, in contrast to primary headache disorders, which appear to be rather homogeneous in their distribution. Thus, genetics, as well as environment including dietary patterns, exposure to pollutants, and infections could potentially be the underlying reason for these disorders. Considering the increasing burden of these disorders, it is necessary for epidemiologists and basic researchers to uncover how environmental influences may be driving the changing patterns seen in disease frequency. Such understanding will be helpful in developing treatment protocols and prevention of neurological disorders.
The rapid and label-free diagnosis of malignancies in ex vivo breast biopsy tissues has significant utility in pathology laboratories and operating rooms. We report a MEMS-based platform integrated with microchips that performs phenotyping of breast biopsy tissues using electrothermal sensing. The microchip, fabricated on a silicon substrate, incorporates a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) as on-chip sensing elements. The microchips are integrated onto the platform using a slide-fit contact enabling quick replacement for biological measurements. The bulk resistivity ( ρ B ), surface resistivity ( ρ S ), and thermal conductivity ( k ) of deparaffinized and formalin-fixed paired tumor and adjacent normal breast biopsy samples from N = 8 patients were measured. For formalin-fixed samples, the mean ρ B for tumors showed a statistically significant fold change of 4.42 ( P = 0.014) when the tissue was heated from 25 °C to 37 °C compared to the adjacent normal tissue, which showed a fold change of 3.47. The mean ρ S measurements also showed a similar trend. The mean k of the formalin-fixed tumor tissues was 0.309 ± 0.02 W m ⁻¹ K ⁻¹ compared to a significantly higher k of 0.563 ± 0.028 W m ⁻¹ K ⁻¹ for the adjacent normal tissues. A similar trend was observed in ρ B , ρ S , and k for the deparaffinized tissue samples. An analysis of a combination of ρ B , ρ S , and k using Fisher’s combined probability test and linear regression suggests the advantage of using all three parameters simultaneously for distinguishing tumors from adjacent normal tissues with higher statistical significance.
The fabrication of integrated circuits (ICs) employing two-dimensional (2D) materials is a major goal of semiconductor industry for the next decade, as it may allow the extension of the Moore’s law, aids in in-memory computing and enables the fabrication of advanced devices beyond conventional complementary metal-oxide-semiconductor (CMOS) technology. However, most circuital demonstrations so far utilizing 2D materials employ methods such as mechanical exfoliation that are not up-scalable for wafer-level fabrication, and their application could achieve only simple functionalities such as logic gates. Here, we present the fabrication of a crossbar array of memristors using multilayer hexagonal boron nitride (h-BN) as dielectric, that exhibit analog bipolar resistive switching in >96% of devices, which is ideal for the implementation of multi-state memory element in most of the neural networks, edge computing and machine learning applications. Instead of only using this memristive crossbar array to solve a simple logical problem, here we go a step beyond and present the combination of this h-BN crossbar array with CMOS circuitry to implement extreme learning machine (ELM) algorithm. The CMOS circuit is used to design the encoder unit, and a h-BN crossbar array of 2D hexagonal boron nitride (h-BN) based memristors is used to implement the decoder functionality. The proposed hybrid architecture is demonstrated for complex audio, image, and other non-linear classification tasks on real-time datasets.
Rare diseases (RD) are conditions that affect a small number of people and hence do not get the focus on government health priorities in a resource-constrained setting such as India. Therefore, it is essential to focus on strengthening and utilizing the existing public health framework for the optimal usage of healthcare resources. In this regard, National Health Mission (NHM) is one of the crucial programs initiated by the government of India to address the health needs of the under-served. As Phase 1 of the NHM moves towards completion, we explored the Reproductive, Maternal, Newborn, Child, and Adolescent Health (RMNCH + A) program under NHM to assess their potential and limitations to aid RD care. We found that some of the disease-prevention initiatives of NHM address certain RDs and can easily be expanded to manage many such preventable RDs. In addition, NHM programs can provide a unique epidemiological data repository to strengthen the National Rare Disease Registry. These programs can also play important role in providing a continuum of care for many RDs that need lifelong management. However, existing programs have a limited scope to provide specialized RD-related treatments, which is better served in a more focused system. Thus, considering RDs in the design of the existing programs may help RD management better through prevention, data collection, and providing a continuum of care.
Acetylcholinesterase (AChE) inhibitors increase the retention of acetylcholine (ACh) in synapses. Although they alleviate cognitive deficits in Alzheimer’s disease, their limited benefits warrant investigations of plant extracts with similar properties. We studied the anti-AChE activity of Convolvulus pluricaulis (CP) in a zebrafish model of cognitive impairment induced by scopolamine (SCOP). CP is a perennial herb with anti-amnesiac and anxiolytic properties. It contains alkaloid, anthocyanin, coumarin, flavonoid, phytosterol and triterpenoid components. Isoxazole (ISOX) was used as a positive control for AChE inhibition. CP-treated 168 hpf larvae showed a similar pattern of AChE inhibition (in the myelencephalon and somites) as that of ISOX-treated larvae. CP was superior to ISOX as evidenced by the retention of avoidance response behavior in adult zebrafish. Molecular docking studies indicated that ISOX binds Ser203 of the catalytic triad on the human AChE. The active components of CP—scopoletin and kaempferol—were bound by His447 of the catalytic triad, the anionic subsite of the catalytic center, and the peripheral anionic site. This suggested the ability of CP to mediate both competitive and non-competitive modes of inhibition. Surprisingly, SCOP showed AChE inhibition in larvae, possibly mediated via the choline-binding sites. CP + SCOP induced a concentration-dependent increase in AChE inhibition and ACh depletion. Abnormal motor responses were observed with ISOX, CP, ISOX + SCOP, and CP + SCOP, indicative of undesirable effects on the peripheral cholinergic system. Our study proposes the examination of CP, SCOP, and CP + SCOP as potential AChE inhibitors for their ability to modulate cognitive deficits.
Arsenic (As) contamination is a major global environmental concern with widespread effects on health of living organisms including humans. In this review, the occurrence (sources and forms) of As representing diverse aquatic habitats ranging from groundwater to marine environment has been detailed. We have provided a mechanistic synopsis on direct or indirect effects of As on different organismal groups spanning from bacteria, algae, phytoplankton, zooplankton and higher trophic levels based on a review of large number of available literature. In particular, special emphasis has been laid on finfishes and shellfishes which are routinely consumed by humans. As part of this review, we have also provided an overview of the broadly used methods that have been employed to detect As across ecosystems and organismal groups. We also report that the use of As metabolites as an index for tracking As tot exposure in humans require more global attention. Besides, in this review we have also highlighted the need to integrate ‘omics’ based approaches, integration of third and fourth generation sequencing technologies for effective pan-geographical monitoring of human gut microbiome so as to understand effects and resulting consequences of As bioaccumulation.
Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
Standing wave thermoacoustic refrigerator uses stack, is the heart of the thermoacoustic cooling system. The porous stack in the resonator tube develops temperature difference across the stack for heat pumping upon loudspeaker sound interaction of oscillating gas. In this paper, the optimization of stack-heat exchangers system and resonator is discussed using linear thermoacoustic theory for better COP and cooling power of refrigerator. The loudspeaker is assumed to provide the required acoustic power with the back volume gas spring system. Helium and air are chosen because of their better thermophysical properties and cost, compared to other competent gases. The 200 mm diameter stack is optimized for the temperature difference of 28 K. The theoretical results of the optimized refrigerator models are compared with the DeltaEC simulation results for deriving conclusions. DeltaEC predicts the cooling power and COP of 349 W at 0.998 for helium, and 139 W at 1.133 for air, respectively.
Our vision is sharpest at the centre of our gaze and becomes progressively blurry into the periphery. It is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme is actually optimal for object recognition? To test this hypothesis, we trained deep neural networks on “foveated” images mimicking how our eyes sample the visual field: objects (wherever they were in the image) were sampled at high resolution, and their surroundings were sampled with decreasing resolution away from the objects. Remarkably, networks trained with the known human peripheral blur profile yielded the best performance compared to networks trained on shallower and steeper blur profiles, and compared to baseline state-of-the-art networks trained on full resolution images. This improvement, although slight, is noteworthy since the state-of-the-art networks are already trained to saturation on these datasets. When we tested human subjects on object categorization, their accuracy deteriorated only for steeper blur profiles, which is expected since they already have peripheral blur in their eyes. Taken together, our results suggest that blurry peripheral vision may have evolved to optimize object recognition rather than merely due to wiring constraints.
Arsenic (As) presence in different environments is attributed to anthropogenic, biogenic and geogenic activities, and it exhibits different toxicity levels depending on its oxidative state and presence in the environment. It also affects water health by changing its alkalinity, oxidation–reduction potential and may also react with other toxic metals and become more toxic in combination with them. As contamination has become a worldwide concern due to adverse effects on health like cardiovascular as well as haematological effects, neurological effects and skin lesions. Due to severe health issues, the development of As removal technologies has become of utmost importance. This review intends to provide information about the source and impact of As on the environment, contamination status of arsenic in different sources and the effect of other heavy metals. We evaluated various effects and consequences of As on soil fertility, plants, photosynthetic pigments, nitrogen metabolism, antioxidants system and biologically active compounds. The impactful use of sustainable monitoring and removal solutions could be an essential and economical way to address the problems of As toxicity in the environment.
Chromium is detected in most ecosystems due to the increased anthropogenic activities in addition to that developed from natural pollution. Chromium contamination in the food chain results due to its persistent and non-degradable nature. The release of chromium in the ecosystem accretes and thereafter impacts different life forms, including humans, aquatic and terrestrial organisms. Leaching of chromium into the ground and surface water triggers several health ailments, such as dermatitis, eczematous skin, allergic reactions, mucous and skin membrane ulcerations, allergic asthmatic reactions, bronchial carcinoma and gastroenteritis. Physiological and biological treatments for the removal of chromium have been discussed in depth in the present communication. Adsorption and biological treatment methods are proven to be alternatives to chemical removal techniques in terms of cost-effectiveness and low sludge formation. Chromium sensing is an alternative approach for regular monitoring of chromium in different water bodies. This review intended to explore different classes of sensors for chromium monitoring. However, the spectrochemical methods are more sensitive in chromium ions sensing than electrochemical methods. Future study should focus on miniaturization for portability and on-site measurements without requiring a large instrument provides a good aspect for future research
The rapidly depleting fossil fuel reserves with rising greenhouse gas levels (GHGs) in the atmosphere necessitate exploring alternate sustainable energy options. Biofuels from microalgae are emerging as a viable renewable energy resource owing to their inherent characteristics of higher biomass and lipid yield per hectare compared to other terrestrial bioenergy feedstocks. In this context, the present communication highlights the prospects of microalgal biofuel and other value-added products produced in a decentralized microalgal biorefinery in the flood plains (gazani lands) of the west coast of India. The spatial extent of potential sites for diatom cultivation estimated in three districts along the Indian west coast was 1940 ha. The opportunities for establishing biorefineries using diatoms as renewable bioenergy feedstocks were investigated through species prioritization, seasonal availability, tolerance, and biochemical composition analyses. Nitzschia and Amphora sp. were prioritized for lab-scale productivity studies based on their tolerance and macromolecular composition. When cultivated in a prototype biofilms-based bioreactor designed using gravel stones as substrates, Amphora sp. Yielded 16 times more productivity (0.56 g L⁻¹) than conventional shake flask cultures. Design of a diatom biorefinery and its mass budgeting considering 100 kg dry biomass yielded ∼15–24 kg of biodiesel. Techno-economic assessment of biodiesel with value-added products of glycerol, biogas, and biofertilizer demonstrated a biodiesel production cost of 30.08–59.52 INR/kg of biodiesel. Harvesting cost in a hybrid mode using mechanized scrubbers and manual labour was estimated as 20 INR/kg of biomass.
Recent studies demonstrated the capability of Sentinel-2 (S2) data to estimate topsoil properties and highlighted the sensitivity of these estimations to soil surface conditions depending on the S2 acquisition date. These estimations are based on Bottom of Atmosphere (BOA) reflectance images, obtained from Top of Atmosphere (TOA) reflectance values using Atmospheric Correction (AC) methods. AC of optical satellite imagery is an important pre-processing stage before estimating biophysical variables, and several AC methods are currently operational to perform such conversion. This study aims at evaluating the sensitivity of topsoil clay content estimation to atmospheric corrections along an S2 time series. Three AC methods were tested (MAJA, Sen2Cor, and LaSRC) on a time series of eleven Sentinel-2 images acquired over a cultivated region in India (Karnataka State) from February 2017 to June 2017. Multiple Linear Regression models were built using clay content analyzed from topsoil samples collected over bare soil pixels and corresponding BOA reflectance data. The influence of AC methods was also analysed depending on bare soil pixels selections based on two spectral indices and several thresholds: the normalized difference vegetation index (NDVI below 0.25, 0.3 and 0.35) and the combination of NDVI (below 0.3) and Normalized Burned Ratio 2 index (NBR2 below 0.09, 0.12 and 0.15) for masking green vegetation, crop residues and soil moisture. First, this work highlighted that regression models were more sensitive to acquisition date than to AC method, suggesting that soil surface conditions were more influent on clay content estimation models than variability among atmospheric corrections. Secondly, no AC method outperformed other methods for clay content estimation, and the performances of regression models varied mostly depending on the bare soil pixels selection used to calibrate the regression models. Finally, differences in BOA reflectance among AC methods for the same acquisition date led to differences in NDVI and NBR2, and hence in bare soil coverage identification and subsequent topsoil clay content mapping coverage. Thus, selecting S2 images with respect to the acquisition date appears to be a more critical step than selecting an AC method, to ensure optimal retrieval accuracy when mapping topsoil properties assumed to be relatively stable over time.
The nanoparticle surfaces interact readily with biomolecules, including proteins forming a corona. The protein layer that directly interacts with the nanoparticle surface is usually explored. Non-covalent interactions of proteins in the second layer have not been elucidated. We report for the first-time multilayer protein-protein interactions in ubiquitin corona on gold nanorod surface. The conformational dynamics of the protein, ubiquitin, on the surface of gold nanorods were probed at the molecular level using two-dimensional solution nuclear magnetic resonance spectroscopy. The interaction of Ubiquitin with gold nanorods was characterized by UV–visible spectroscopy, dynamic light scattering, and zeta-potential measurements. The residues of Ubiquitin that show significant perturbations fall into two categories: (a) the residues that directly interact with the surface of gold nanorods forming hard corona (primary interaction) and (b) the residues that are forming soft corona (secondary interaction). The formation of hard corona on the surface of the nanorod takes place even at a low concentration of the protein. The protein does not undergo any changes in its secondary or tertiary structure. As the protein concentration is increased, the hard corona, which is off mutually interconvertible multiple conformers, undergoes conformational reorientation to maximize its interaction with the nanorod surface. The study reveals electrostatically driven weak protein-nanorod interactions. Further, the study reveals two different processes, one involving the direct interaction with nanorods and the other involving protein corona formation in the protein-nanomaterial system. The study, for the first time, explores multilayer protein corona on a nanoparticle surface and provides new insights into the mechanisms involving protein aggregation in the presence of the nanoparticles.
The question of how soft polymers slide against hard surfaces is of significant scientific interest, given its practical implications. Specifically, such systems commonly show interesting stick–slip dynamics, wherein the interface moves intermittently despite uniform remote loading. The year 2021 marked the 50th anniversary of the publication of a seminal paper by Adolf Schallamach ( Wear , 1971), which first revealed an intimate link between stick–slip and moving detachment waves, now called Schallamach waves. We place Schallamach’s results in a broader context and review subsequent investigations of stick–slip, before discussing recent observations of solitary Schallamach waves. This variant is not observable in standard contacts so that a special cylindrical contact must be used to quantify its properties. The latter configuration also reveals the occurrence of a dual wave—the so-called separation pulse—that propagates in a direction opposite to Schallamach waves. We show how the dual wave and other, more general, Schallamach-type waves can be described using continuum theory and provide pointers for future research. In the process, fundamental analogues of Schallamach-type waves emerge in nanoscale mechanics and interface fracture. The result is an ongoing application of lessons learnt from Schallamach-type waves to better understand these latter phenomena. This article is part of the theme issue ‘Nanocracks in nature and industry’.
Gamma oscillations (30–70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike–field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike–gamma phase estimation. I then discuss the expected spike–gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike–gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2–4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory–inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
The use of bacterial biochar for Cr(VI) removal opens room for new research in terms of cost-effectiveness, better performance and high selectivity. This work reports the synthesis, physicochemical characterization and performance evaluation of bacterial biochar synthesized at 200 °C and 400 °C. To examine the removal mechanism and behaviour, batch mode experiments were conducted by modifying different parameters such as dosage, pH, initial ion concentration and temperature. The capacity of the biochar for removing Cr(VI) was found to be qmax = 19.43 mg/g (200 °C) and 29.73 mg/g (400 °C). The BET surface area of bacterial biochar (400 °C) is also seen to decrease from 22.09 m²/g to 3.17 m²/g. The decrease in pore volume and surface area makes successful adsorption evident. In addition to this, the adsorption data were adequately simulated with Langmuir, Freundlich and Temkin and pseudo-second kinetics suggesting that the adsorption process were the combination of external mass transfer and chemisorption. Electrostatic interactions were determined to be the dominant removal mechanism. Biochar pyrolyzed at 400 °C serves as good adsorbent material to remove Cr(VI) from aqueous solutions. It can also be a suitable material for removing emerging contaminants from aqueous solutions.
Small firms' performance has been recognised as an important topic for researchers dealing with the topics of internationalisation and innovation. Literature has examined the individual influence of network cooperation, innovation and internationalisation on firm performance. However, there is an absence of research to explore the coherent relationship between network linkages, innovation performance, internationalisation performance and its cumulative influence on economic performance. That is, this research examines the mediating roles of innovation and internationalisation between network cooperation and firm performance. Based on the sample of 117 exporting Indian SMEs and using structural equation modelling, the results note that indirect effects produced by customers and Research and Development (R&D) organisations via innovation performance explain a higher proportion of their total effect on the economic performance of SMEs. Conversely, the relationship between three network stakeholders, viz. customers, government agencies and R&D organisations, and economic performance are mediated by the internationalisation performance of SMEs.
In this work, the destruction of toluene as a biomass tar model compound has been investigated in a rotating gliding arc (RGA) plasma catalytic system focusing on understanding the contribution of typical transition metals (Fe, Co, Cu) in Ni-based bimetallic catalyst. Investigations were conducted to elucidate their synergy with plasma under simulated gasifier gas (SGG) to destruct toluene and their effect on value-added benefits such as the enhanced heat content of the reacted producer gas. Results showed that the N2 environment offered better performance than the SGG environment, especially at high tar concentration, due to a more abundance of N2 excited species. The loading of Ni on the Al2O3 catalyst remarkably enhanced the tar conversion from 80.7% to 93.1%. Except for the NiFe, the bimetallic catalysts improved conversion and reduced specific energy consumption (SEC). Primarily, the NiCu catalyst provided a maximum tar conversion of up to 94.3% and significantly enhanced the heat content of the producer gas by 29% from that of the SGG. The minimum SEC of 64.5 kWh/kg was achieved by the NiCo, which also showed the best sintering resistance. In the 24-hour plasma-catalytic operation, NiCu and NiCo showed excellent stability with only a slight drop in the tar conversion (∼94% to ∼ 91%) after 10–12 h. Analysis of by-products indicated back spillover of OH and O, which could help clean the metal surface. Thermogravimetric analysis of the spent catalyst indicated that the coke deposited is likely composed of the aromatic compounds of boiling point in the range of 100 °C to 300 °C.
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