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Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication and behavioral challenges. It is a lifelong neurodevelopmental condition with its onset before the age of three years. It is characterized by abnormalities in communication, impaired social function, repetitive behaviors and restricted interests. Western medicine and research have seemingly stalled in respect to the management of autism however early intervention and behavioral therapies have shown improvement to some extent. The present study describes the case of childhood autism visited at Government Ayurvedic Medical College and Hospital, Akhnoor, Jammu. Child was diagnosed clinically and has been treated with Ayurvedic interventions, Panchakarma therapies and diet modification. Child has got relief symptomatically within one week of therapy started. Case study briefly explained Ayurvedic concepts regarding childhood autism and Ayurvedic treatment protocols in autistic disorder. Keywords: Autism Spectrum Disorder, hyperactivity, Ayurveda, Management, Prevention, Pinda Sweda, Abhyanga, Pichu, Nasya, Uttar Basti.

Malaria is a major public health issue that affects people all around the world. The diagnosis of red blood cells contaminated with insects under a microscope by a skilled specialist is a typical way of diagnosing malaria. This method does not function effectively, and the diagnosis is made based on the test taker's experience and expertise. Malaria blood tests have been using automated imaging technologies based on machine learning for early detection. However, thus far, practical performance is insufficient. The convolutional neural network CNN is used in this paper to present an innovative and resilient machine learning model. for automatically distinguishing single cells in tiny blood smears from normal microscope slides, such as infected or non-infected with this virus. Our new CNN model's average accuracy for 16 layers is 97.37 percent based on ten-fold confirmation using 27,578 single-image pictures. In the same photos, the learning transfer model only gets 91.99 percent. All performance indicators, such as sensitivity (96.99 percent vs 89.00 percent), clarity (97.75 percent vs 94.98 percent), accuracy (97.73 percent vs 95.12 percent), F1 school (97.36 percent vs 90.24) percent, and Matthew's correlation coefficient, demonstrate that the CNN model outperforms the transfer learning model (94.75 percent vs 85.25 percent). Index terms: Deep Learning; Convolutional Neural Networks; Malaria; Computer-Aided Diagnostics; Machine Learning

Sometimes, outcomes of random processes don’t seem to follow the theoretical probabilities due to the presence of bias and even when the probabilities are followed in a large number of trials, dynamic bias is still evident in many of these processes. This paper provides a short study on the bias using examples and defines what kind of processes could be biased. It also demonstrates the two types of bias which are dynamic and fixed. This study could be used to analyze the bias in various random processes and get a better understanding of the outcomes. Dynamic bias has further been explained with the help of 52 cards. The study helps in providing a better understanding of randomness and further helps in designing experiments.

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

Telescopic Handlers, also known as Telehandlers or teleporters or boom lifts are small human-operated machines extensively used by the agricultural, construction and landscaping industries. Different types of attachments are being used with these telehandlers which make them extremely versatile, safe, swift and costumer friendly. They prove out to be very efficient when used with various attachments designed as per the market requirements. Common attachments include forks, lifting jibs, man-basket or work platforms, buckets etc which are attached to the telescopic boom of the telehandlers as per use. However, these attachments should be chosen with utmost care keeping in mind that it does not pose any threat on the operation safety. While, these telehandlers have numerous advantages and applications, they also have certain limitations which slightly restricts their use in some cases. The present work is a review on telescopic handlers, their applications, limitations and future scope of research on their improvement to expand the domain of the usage.

This paper analyzes performance of Artificial Intelligence based optimization controller for the comparative study of maximum power point tracking (MPPT) in PV Systems. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) control methods are the two such techniques used, and are simulated in MATLAB-Simulink using Trina Solar TSM-250PD05.08. The simulation results suitably depict the performance of these methods on the basis of some parameters like their rise time, settling time, time taken to reach maximum power point and their efficiency. It is found that maximum power point is tracked in PV systems with greater efficiency using PSO as compared to ANN.

The proposed work implements a hybrid model design in matlab software 2015a tool. The problem of stability in power distribution is resolved by using PI controller. The design is based on Solar and thermal power plant combination and concept of negative feedback control system. For the appraisal the item greenius was used, a program being reliably made at DLR since 1999. This program didn't have a limit work for PV diversions. Appropriately one of the purposes of this hypothesis was at first to dismember the precision of the PV show and thus make and execute a battery model to survey the budgetary sensibility of autonomous PV structures. Consequently, the greenius model was differentiated and PV-Watts and PVSyst photovoltaic models. Likewise, an electric amassing segment subject to a charge balance model was executed in the structure. This model showed to definitely depict the introduction of batteries without the prerequisite for certain parameters. The second purpose of this proposition was to update and evaluate the cash related credibility of crossbreed impact plants with a high breaking point factor (>90%).

Proper management of municipal solid waste is one of the prime matters of concern for metropolitan cities. To be able to successfully manage the solid waste generated, we need to plan in advance. A very essential pre-requirement for an efficient solid waste management is an accurate prediction of the garbage generation. Accurate forecasting of the quantity of MSW generation will enable us to design and operate an effective waste collection system. The main objective of this paper is to compare different models of artificial intelligence, viz. artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), discrete wavelet theory–artificial neural network (DWT-ANN), discrete wavelet theory–adaptive neuro-fuzzy inference system (DWT-ANFIS), genetic algorithm–artificial neural network (GA-ANN) and genetic algorithm–adaptive neuro-fuzzy inference system (GA-ANFIS) to examine and evaluate their capability in forecasting the amount of garbage being generated. A case example of the city of New Delhi, India, has been used for better understanding of different models. Root mean square error (RMSE), coefficient of determination (R2) and index of agreement (IA) values for every model were calculated, and the models were compared on the basis of it. The hybrid model of genetic algorithm and artificial neural network was found to have the lowest RMSE, the highest IA value and the highest R2 values, and hence is the most accurate of the above six models.

Every 6 minutes a child suffers a staircase-related injury as per a new study mentioned in an article by WebMD,. In this paper, we aim to show how various human activities can be identified and monitored with the help of a Signal Processing and Machine Learning techniques. The proposed algorithm demonstrates how different movements can be expressed in terms of signals and subsequently differentiated and identified. Several goal-specific algorithms are utilized, based on feature extraction and classification to aid in identification of the actions.

Proteins provide versatile mediators for assembling nanoparticles into organized composites. Here, we demonstrate that a single protein spacer (lysozyme) can be used to direct the self-assembly of gold mixed monolayer protected clusters into controlled ensembles with varied functional response. This efficient self-assembly process provides nanocomposites featuring tunable interparticle spacings, as determined through small angle X-ray scattering, varied sample morphology, as observed by transmission electron microscopy, and modular collective optical behavior, as examined through UV−visible spectroscopic measurements.

Positively charged trimethylammonium-functionalized mixed monolayer protected clusters (MMPCs) bind DNA through complementary electrostatic interactions, resulting in complete inhibition of DNA transcription of T7 RNA polymerase. DNA was released from the nanoparticle by intracellular concentrations of glutathione, resulting in efficient transcription. The restoration of RNA production was dose-dependent in terms of GSH, with considerable control of the release process possible through variation in monolayer structure. This work presents a new approach to controlled release of DNA, with potential applications in the creation of transfection vectors and gene regulation systems.

Biomacromolecule surface recognition is an important factor in regulating cellular processes. Nanometer-scale mixed monolayer-protected clusters (MMPCs) provide scaffolds for creating receptors targeting biomacromolecular surfaces. Unique features of nanoparticles that make them particularly attractive resources for biomacromolecular recognition and their use in modification of structure and function of biomacromolecules are illustrated in this review.

Negatively charged carboxylate-functionalized mixed monolayer protected clusters (MMPCs) effectively bind and inhibit alpha-chymotrypsin based on complementary electrostatic surface recognition. We demonstrate that this binding can be disrupted by varying the ionic strength of the medium. Enzyme activity in the presence of MMPCs increases from 5% to 97% of native activity as salt concentration is increased from 0 to 1.5 M. Variation of ionic strengths after complete binding over 13 h results only in a modest restoration of enzymatic activity (< 35%). The conformation of chymotrypsin was characterized using circular dichroism and fluorescence spectroscopy, correlating structure with enzymatic activity. This work provides a useful insight of the electrostatic influence on protein--MMPC interactions and can be applied toward MMPC-based controlled release of proteins in vivo.

Cytochrome c adsorbed to anionic nanoparticles is selectively proteolyzed by trypsin, providing a mechanism for the catalytic degradation of proteins.

Despite more than two decades of research and development on nucleic acid vaccines, there is still no commercial product for human use. Taking advantage of the recent innovations in systemic delivery of short interfering RNA (siRNA) using lipid nanoparticles (LNPs), we developed a self-amplifying RNA vaccine. Here we show that nonviral delivery of a 9-kb self-amplifying RNA encapsulated within an LNP substantially increased immunogenicity compared with delivery of unformulated RNA. This unique vaccine technology was found to elicit broad, potent, and protective immune responses, that were comparable to a viral delivery technology, but without the inherent limitations of viral vectors. Given the many positive attributes of nucleic acid vaccines, our results suggest that a comprehensive evaluation of nonviral technologies to deliver self-amplifying RNA vaccines is warranted.

The interaction of nanomaterials with cells and lipid bilayers is critical in many applications such as phototherapy, imaging, and drug/gene delivery. These applications require a firm control over nanoparticle-cell interactions, which are mainly dictated by surface properties of nanoparticles. This critical Review presents an understanding of how synthetic and natural chemical moieties on the nanoparticle surface (in addition to nanoparticle shape and size) impact their interaction with lipid bilayers and cells. Challenges for undertaking a systematic study to elucidate nanoparticle-cell interactions are also discussed.

Intracellular trafficking of nanoscale objects is regulated by cell membranes through interactions that are under intense investigation. These materials are typically internalized by cells into membrane-bounded endosomes and never gain access to the cell machinery located in the cytosol. While some bio-macromolecules can penetrate or fuse with cell membranes without overt membrane disruption, no synthetic material of comparable size has shown this property yet. Cationic nanoscale objects (mostly assisted by some degree of hydrophobicity) pass through cell membranes by generating transient holes, a process associated with cytotoxicity. To date, studies aimed at generating cell-penetrating nanomaterials have focused on the effect of size, shape, and composition. Here we compare membrane penetration by two nanoparticle ‘isomers’ with the same composition (same hydrophobic content), one coated with sub-nanometer striations of alternating anionic and hydrophobic groups, and the other coated with the same moieties but in a random distribution. We show that the former particles penetrate the plasma membrane without bilayer disruption while the latter were instead mostly trapped in endosomes. Our results highlight a new design rule for materials that interact with cells and offer a paradigm for analyzing the fundamental problem of cell membrane penetrating bio- and macro-molecules. We anticipate consequences for designing materials for biomedical applications and for understanding potential safety issues associated with synthetic nanomaterials.

Nanoparticles featuring highly flexible sidechains template to peptides, demonstrating substantial pre-organization of the particle monolayer.

Trimethylammonium functionalized gold nanoparticles are demonstrated as templates for the assembly of peptide fragments and their subsequently promoted ligation. This system displays the use of organically tailored nanoparticles as effective supramolecular reagents for catalyzing bond-forming reactions and may also serve as a model for prebiotic conditions where charged surfaces may have promoted the polymerization of the early biopolymers.

Positively charged trimethylammonium-functionalized mixed monolayer protected clusters (MMPCs) of different chain lengths (C(8) and C(11)) have been used to bind beta-galactosidase through complementary electrostatic interactions, resulting in complete enzyme inhibition. This inhibition can be reversed in vitro by intracellular concentrations of glutathione (GSH), the main thiol component of the cell. The restoration of activity depends on the chain length of the monolayer. The activity of enzyme bound to particles with C(8) monolayer was completely restored by intracellular concentrations (1-10 mM) of GSH; however, little or no release was observed at extracellular GSH concentrations. In contrast, no restoration was observed for enzyme bound to the C(11) particles at any of the concentrations studied. Taken together, these studies demonstrate that the GSH-mediated release of enzymes bound to MMPCs can be tuned through the structure of the monolayer, a significant tool for protein and drug delivery applications.

alpha-Helices are important structural elements in proteins. To provide a scaffold for the facial recognition of peptides, we have explored the interaction of cationic mixed monolayer protected clusters (MMPCs) with a tetra-aspartate peptide in water. In these studies, substantial enhancement of peptide helicity was observed upon addition of the MMPC. Significantly, this stabilization increased with time, demonstrating templation of the monolayer to the peptide helix.

Thioalkyl and thioalkylated oligo(ethylene glycol) (OEG) ligands with chain-end functionality were used to fabricate water-soluble CdSe nanoparticle scaffolds. Surface recognition of chymotrypsin (ChT) was achieved using these functionalized nanoparticle scaffolds, with three levels of interaction demonstrated: no interaction (OEG terminated with hydroxyl group), inhibition with denaturation (carboxylate-terminated thioalkyl ligands), and inhibition with retention of structure (carboxylate-terminated OEG). The latter process was reversible upon an increase in ionic strength, with essentially complete restoration of enzymatic activity.

Anionically functionalized amphiphilic nanoparticles efficiently inhibit chymotrypsin through electrostatic binding followed by protein denaturation. We demonstrate the ability to disrupt this "irreversible" inhibition of chymotrypsin through modification of the nanoparticle surface using cationic surfactants. Up to 50% of original chymotrypsin activity is rescued upon long-chain surfactant addition. Dynamic light-scattering studies demonstrate that chymotrypsin is released from the nanoparticle surface. The conformation of the rescued chymotrypsin was characterized by fluorescence and fluorescence anisotropy, indicating that chymotrypsin regains a high degree of native structure upon surfactant addition.

Results We evaluated systemic and mucosal immune responses in mice and rabbits using the SAMTM platform expressing HIV-1 gp140 (HIV-SAMTM vaccine) prime, protein/MF59 vaccine boost regimen for both HIV-1 Clade B and C Env antigens. In mice, the primed Env-specific IgG response to 1 μg of the HIV-SAMTM vaccine was comparable to a 10 μg dose of an identically formulated DNA vaccine, 10(7) IU of VRP, and 10 μg protein/MF59 vaccines. The HIVSAMTM vaccine primed response could be boosted robustly by a protein/MF59 vaccine and resulted in a balanced IgG1, IgG2a subclass response, similar to that seen with the VRP vaccine, but unlike the dominant IgG1 response to protein/MF59 only vaccinations. Both Envspecific CD4+ and CD8+ T-cell responses were detectable after two HIV-SAMTM vaccinations. A TH1 type (IFNg+, IL-5-) profile was demonstrable for the HIV-SAMTM vaccine primed, protein boosted CD4+ T-cell response, similar to that seen with the DNA or VRP primed protein boosted responses, in contrast to a TH2 type (IFNglow, IL-5+) response seen with protein/MF59 vaccination. In rabbits, priming with the 25 or 50 μg of the formulated HIV-SAMTM vaccine induced robust and avid Env-binding IgG and HIV neutralizing antibodies that were superior to 500 μg of an unformulated DNA vaccine and comparable to VRP and protein/MF59 vaccines. In addition, protein/ MF59 boostable Env-specific vaginal wash Ig was consistently demonstrable in both mice and rabbits immunized with the HIV-SAMTM.

Monolayer-protected nanoparticles feature tunable size, surface functionality and core material, providing scaffolds for targeting biomacromolecules. This review highlights recent advances in nanoparticle-biomacromolecule interactions, focusing on two key areas: (1) The modulation of structure and function of biomacromolecules through engineered interactions with nanoparticle surfaces; (2) The use of biomacromolecules as building blocks for nanostructured materials.

We have used mixed monolayer protected gold clusters (MMPCs) to provide flavoenzyme model systems with a high affinity and ability to modulate cofactor reduction potential.

Nanoparticles present a versatile scaffold to target biomacromolecule surfaces via complementary interactions. This review highlights some unique features of nanoparticles that make them particularly attractive resources for biomacromolecular recognition, and displays their use in modulation of structure and function of biomacromolecules.