Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data.

BACKGROUND: In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism. NEW METHOD: The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism. RESULTS: The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas. COMPARISON WITH EXISTING METHOD (S): The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%. CONCLUSIONS: This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

Statistical Analyses for Key Risk Factor Identification and Prediction of Chronic Kidney Disease.

Chronic renal disease, also known as chronic kidney disease (CKD) is a common disease and is a concern of public health management. Effective techniques for early CKD prediction are desirable. Given a set of biomarkers, Machine learning techniques are known for predicting CKD. This work aims of predicting CKD given clinical data. The proposed work suggests a methodology that includes data prepossessing (i.e. data cleaning, addressing null values and normalizing), applying statistical methodologies for finding key risk factors. Finally, using the most significant risk factors, machine learning techniques is applied to prognosticate the onset of CKD. The proposed approach has been tested with two data sets and proves to be fast, cost-effective and accurate compared to the existing state of the art techniques.Clinical relevance- Prognosticating CKD with a higher accuracy using a minimum number of risk factors is a significant aspects of healthcare informatics, where the treatment cost and predictive results both are optimized towards the betterment of patients.

ALERT: Atlas-Based Low Estimation Rank Tensor Approach to Detect Autism Spectrum Disorder.

In response to a stimulus, distinct areas of the human brain are activated. Also, it is known that the regions interact with one another. This functional connectivity is helpful to diagnose any neurological abnormality, such as autism spectrum disorder (ASD). This work proposes an approach to construct a functional connectivity network from fMRI image data. For obtaining a functional connectivity network, the time series component of fMRI data is used and from it correlation matrix is calculated showing the degree of interaction among the brain regions. To map the different regions of a brain, the brain atlas is considered. This essentially yields a low-rank tensor approximation of the functional connectivity matrix. A 2D convolutional deep neural network model is built to categorize topological similarity in the functional connectivity matrices related to ASD and typically developing control. The proposed approach has been tested with ABIDE dataset of fMRI data for autism spectrum disorder. Several brain atlases have been considered in the experiment. With a majority voting concept on the results from the atlases, the proposed technique reveals an ASD detection accuracy of 84.79%, which is significantly comparable to the state of the art techniques.Clinical Relevance- ASD is one of the least understood neurological disorders that has been recently recognized to have major sociological consequences on an affected individual's life. A symptom-based diagnosis is in practice. However, this requires prolonged behavioural examinations under the supervision of a highly skilled multidisciplinary team. An early and cost-effective detection using an fMRI image is considered an appropriate, comprehensive, and advanced treatment plan.

Improving Hydrophobicity of Collagen with Silica Nanoparticles: Probing a Noncovalent Approach.

Collagen-based materials have a wide range of applications in wound care, tendon repair, cartilage repair, etc. Improving certain properties such as hydrophobicity can diversify the application areas. In this work, we investigated the noncovalent interactions of suitably functionalized silica nanoparticles with collagen for the possibility of improving hydrophobicity. Functionalization on silica nanoparticles was achieved via Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) or "click" reaction using surface grafting methods. Furthermore, we synthesized two different silica nanoparticles (SiNPs) functionalized with the fluorine-containing substrate or only with an aryl moiety (silica-g-4EMB and silica-g-ETFMB) for comparison. The functionalized SiNPs immobilized along with the model system trans-4-hydroxy-l-proline (HPA) (usually present in abundant quantities in collagen) have been probed using nuclear magnetic resonance (NMR) spin relaxation to appreciate the influence of SiNPs on HPA. Furthermore, we effectively utilized a saturated transfer difference (STD) NMR experiment to measure the interaction parameters between judiciously functionalized silica nanoparticles and substrates of interest. In essence, such a detailed study on noncovalent interactions employing an arsenal of experimental approaches facilitated the immobilization of suitably functionalized silica nanoparticles to collagen and leather (where collagen is a major constituent), leading to improvements in hydrophobicity.

Transparent Superhydrophobic Coatings of Silica Nanoparticles Using Functionalized Polyurethanes.

Superhydrophobic coatings are essential to prepare water-repellent surfaces, self-cleaning materials, etc. Silica nano-materials are often immobilized to different surfaces for imparting super-hydrophobicity. Direct coating of silica-nanoparticles is often challenging since it can easily be peeled off under different environments. Herein, we reported the use of properly functionalized polyurethanes to facilitate the strong binding of silica-nanoparticles to surfaces. The alkyne terminal polyurethane was synthesized by step-growth polymerization while click-reactions facilitated to post-functionalization using phenyl moiety and were characterized by 1 H, 13 C nuclear magnetic resonance (NMR) spectroscopies, and 1 H spin-lattice relaxation times (T1 s). Upon functionalization, the glass transition temperature (Tg) increased due to enhanced interchain interactions. Moreover, additives like di(propyleneglycol)dibenzoate showed a substantial plasticizing effect to compensate for the increase in Tg, an important parameter for low-temperature applications. NMR signatures the spatial interactions between various protons of grafted silica-nanoparticles and phenyl triazole functionalized polyurethanes, thus indicating the usefulness of polyurethanes to bind silica-nanoparticles. After coating functionalized silica-nanoparticles to leather using functionalized polyurethanes, a contact angle value of more than 157° was observed with retention of grain patterns of leather due to transparency. We anticipate the results to help design varieties of materials with superhydrophobicity where the structural integrity of the surfaces is retained.

Functionalization of Bacterial Cellulose and Related Surfaces Using a Facile Coupling Reaction by Thermoresponsive Catalyst.

Recently, bacterial cellulose and related materials attracted significant attention for applications such as leather-like materials, wound healing materials, etc., due to their abundance in pure form and excellent biocompatibility. Chemical modification of bacterial cellulose further helps to improve specific properties for practical utility and economic viability. However, in most cases, chemical modification of cellulose materials involves harsh experimental conditions such as higher temperatures or organic solvents, which may destroy the 3-dimensional network of bacterial cellulose, thereby altering its characteristic properties. Hence, in this work, we have adopted the Suzuki coupling methodology, which is relatively unexplored for chemically modifying cellulose materials. As the Suzuki coupling reaction is tolerable against air and water, modification can be done under mild conditions so that the covalently modified cellulose materials remain intact without destroying their 3-dimensional form. We performed Suzuki coupling reactions on cellulose surfaces using a recently developed thermoresponsive catalyst consisting of poly(N-isopropylacrylamide) (PNIPAM)-tagged N-heterocyclic carbene (NHC)-based palladium(II) complex. The thermoresponsive nature of the catalyst particularly helped to perform reactions in a water medium under mild conditions considering the biological nature of the substrates, where separation of the catalyst can be easily achieved by tuning temperature. The boronic acid derivatives have been chosen to alter the wettability behavior of bacterial cellulose. Bacterial cellulose (BC) obtained from fermentation on a lab scale using a cellulose-producing bacterium called Gluconacetobacter kombuchae (MTCC 6913) under Hestrin-Schramm (HS) medium, or kombucha-derived bacterial cellulose (KBC) obtained from kombucha available in the market or cotton-cellulose (CC) was chosen for the surface functionalization to find the methodology's diversity. Movie files in the Supporting Information and figures in the manuscript demonstrated the utility of the methodology for fluorescent labeling of bacterial cellulose and related materials. Finally, contact angle analysis of the surfaces showed the hydrophobic natures of some functionalized BC-based materials, which are important for the practical use of biomaterials in wet climatic conditions.

Immobilization of Polymers to Surfaces by Click Reaction for Photocatalysis with Recyclability.

A surface-bound photocatalyst offers advantages of reusability and recyclability with ease. While it can be immobilized by spin coating or drop-casting, a more reliable and durable method involves the formation of a self-assembled monolayer (SAM) on a suitable surface using designer molecules. In this paper, we report devising a practical, durable, and recyclable photocatalytic surface using immobilized polytriazoles of diketopyrrolopyrrole (DPP). While the SAM formation techniques were utilized for superior results, conventional coatings of polymers on surfaces were performed for comparison. Different methods confirmed efficient immobilization and high grafting density for the SAM technique. Computational models suggested favorable energy parameters for active materials. Photocatalytic studies were performed using both immobilized polymers and polymers in solution for comparison. These findings are important for understanding various physicochemical characteristics of polytriazole-functionalized surfaces.

SHUBHCHINTAK: An efficient remote health monitoring approach for elderly people.

With the proliferation of IoT technology, it is anticipated that healthcare services, particularly for the elderly persons, will become a major thrust area of research in the coming days. Aim of this work is to design a fit-band containing multiple sensors to provide remote healthcare services for the elderly persons. An application has been designed to capture health data from the fit-band, pre-process the data and then send them to cloud for further analysis. A wireless Bluetooth enabled connection is proposed to establish communications between sensors and the application for data transmission. In the proposed application, there are three different front-end interfaces for three different users: system administrator, patient and doctor. The data collected from the patient's fit-band are sent to a cloud data storage, where the data will be analyzed to detect anomaly (e.g., heart attack, sleep apnea, etc.). A Convolution Neural Network (CNN) model is proposed for anomaly detection. For the classification of anomaly, a Long Short Term Memory (LSTM) model is proposed. In the presence of anomaly, the system immediately connects a doctor through a phone call. A prototype system termed as Shubhchintak has been developed in Android/IOS environment and tested with a number of users. The fit-band provides data tracking with an overall accuracy of 99%; the system provides a response with 3000 requests in less than 100 ms. Also, Shubhchintak provides a real-time feedback with an accuracy of 97%. Shubhchintak is also tested by patients and doctors of a nearby hospital. Shubhchintak is shown to be a simple to use, cost effective, comfortable, and efficient system compared to the existing state of the art solutions.

Bacterial cellulose: A promising biopolymer with interesting properties and applications.

The ever-increasing demands for materials with desirable properties led to the development of materials that impose unfavorable influences on the environment and the ecosystem. Developing a low-cost, durable, and eco-friendly functional material with biological origins has become necessary to avoid these consequences. Bacterial cellulose generated by bacteria dispenses excellent structural and functional properties and satisfies these requirements. BC and BC-derived materials are essential in developing pure and environmentally safe functional materials. This review offers a detailed understanding of the biosynthesis of BC, properties, various functionalization methods, and applicability in biomedical, water treatment, food storage, energy conversion, and energy storage applications.

Correction: A photocatalytic chip inspired from the photovoltaics of polymer-immobilized surfaces: self-assembly and other factors.

Correction for 'A photocatalytic chip inspired from the photovoltaics of polymer-immobilized surfaces: self-assembly and other factors' by Periyamuthu Ramar et al., Chem. Commun., 2021, 57, 12964-12967, https://doi.org/10.1039/D1CC04381A.

A photocatalytic chip inspired from the photovoltaics of polymer-immobilized surfaces: self-assembly and other factors.

Polymers and carbon nanomaterials for bulk heterojunction photovoltaic devices have been used to develop an efficient reusable photocatalytic chip. Interestingly, it is highly effective when the materials are self-assembled in a particular pattern at a particular concentration ratio (Movies in the ESI).

Blocking and Deblocking of Diisocyanate to Synthesize Polyurethanes.

Diisocyanates, particularly toluene diisocyanate (TDI), are useful for the preparation of various polyurethanes with specific applications as leather-like materials, adhesives and insoles, etc. Blocking agents can be used for the operational simplicity and to reduce the hazards of TDI. In this paper, we reported the use of 3-(4-bromo-phenyl)-1H-pyrazole to block toluene diisocyanate (TDI). FTIR, NMR, thermogravimetric analysis, contact angle analysis and differential scanning calorimetry (DSC) were used for the characterization. The effectiveness of the blocking was confirmed by spectroscopic techniques. The DSC thermogram showed that blocked adducts deblock at 240 °C, causing the regeneration of TDI, and causing the diisocyanates to react with polyols of different molecular weights, forming polyurethanes. The characterization of the polyurethanes was performed by infrared spectroscopy, nuclear magnetic resonance spectroscopy, thermogravimetric analysis, differential scanning calorimetry and a contact angle study.

Improved Hydrophobicity of a Bacterial Cellulose Surface: Click Chemistry in Action.

The vast application potentials of bacterial cellulose (BC)-based materials for developing leather-like materials, wound-healing materials and electronic materials have been realized very recently. Surface functionalization of these materials can help in improvement of certain properties such as water repellency, mechanical strength, and so forth. In this paper, we reported functionalization of BC surfaces using "click" polymerization for the first time. By this methodology, dense aromatic groups have been incorporated for the improvement of hydrophobicity. For comparative studies, various fluorine-based compounds have been introduced using conventional click reactions. The surface-modified BC materials have been confirmed by various spectroscopic methods. Particularly, the chemical structures of the materials were studied by solid-state 13C NMR spectroscopy and attenuated total reflection-infrared spectroscopy. X-ray photoelectron spectroscopy was used to study the elemental composition of the materials. Moreover, the crystallite changes of modified BC surfaces were investigated by X-ray diffraction. Further, the changes in the morphology of the material after functionalization were evaluated by scanning electron microscopy and atomic force microscopy. Finally, water contact angle measurement revealed manyfold increase in hydrophobicity after click polymerization. A video is also provided in the Supporting Information to show the application potential of this material for developing leather-like materials.

Weighted sparse representation for classification of motor imagery EEG signals.

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in demand for many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality, movement of wheelchairs, etc. Traditional sparse representation based classification (SRC) is a thriving technique in recent years and has been a successful approach for classifying MI EEG signals. To further improve the capability of SRC, in this paper, a weighted SRC (WSRC) has been proposed for classifying two-class MI tasks (right-hand, right-foot). WSRC constructs a weighted dictionary according to the dissimilarity information between the test data and the training samples. Then for the given test data the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives better discriminative information than SRC and as a consequence, WSRC proves to be superior for MI EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.

Removal of Eye Blink Artifacts From EEG Signals Using Sparsity.

Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain-computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.

Scalable Synthesis of Hide Substance-Chitosan-Hydroxyapatite: Novel Biocomposite from Industrial Wastes and Its Efficiency in Dye Removal.

A novel porous polymer-inorganic hybrid biocomposite with various functional groups (hide substance/chitosan/hydroxyapatite) has been synthesized in simple, economic, and scalable process utilizing leather industry solid waste and seafood industry waste composed with hydroxyapatite. Physicochemical characterization of the material reveals formation of composites with homogenous distribution of the constituents in the material matrix. The composite is hard and porous (with 0.1632 cm3/g slit-shaped mesopores and micropores) having particle sizes 40-80 µm and a Brunauer-Emmett-Teller surface area of 55.54 m2/g. The material is polycrystalline in nature with a fair amount of amorphous substance and less hydrophilic in character than constituent polymers. The dye removal efficiency of the material has been tested with two model dyes, namely, methylene blue (MB) (cationic/basic dye) and sunset yellow (SY) (anionic/acid dye). Optimum adsorptions of 3.8 mg MB (pH 12, RT ≈ 27 °C) and 168 mg of SY (pH 3, RT ≈ 27 °C) have been found per gram of the composite material. Langmuir isotherm and pseudo second order rate models have been found to be the best-fit models to explain the equilibrium isotherm and kinetics of the adsorption process for both the dyes. However, higher and faster adsorption of SY in comparison with MB indicated higher binding efficiency of the material toward the acidic dye. Desorption of dyes from the dye-adsorbed material was studied using a suitable eluent of appropriate pH and recycling for five times showed without loss of efficiency. The prepared composite showed very high dye removal efficiency toward four different commercially used dyes (496 mg/g of Orange-NR, 477 mg/g of Red-VLN, 488 mg/g of Blue-113 dye, and 274 mg/g of Green-PbS dye) from their individual and cocktail solutions. It was also efficient to decolorize dye-bearing tannery exhaust bath. Hence, waste materials generated during industrial processes could be efficiently used for the decontamination of colored wastewater produced by various industries.

Experimental and Theoretical Investigations of Different Diketopyrrolopyrrole-Based Polymers.

Diketopyrrolopyrrole (DPP)-based polymers are often considered as the most promising donor moiety in traditional bulk heterojunction solar cell devices. In this paper, we report the synthesis, characterization of various DPP-based copolymers with different molecular weights, and polydispersity where other aromatic repeating units (phenyl or thiophene based) are connected by alternate double bonds or triple bonds. Some of the copolymers were used for device fabrication and the crucial parameters such as fill factor (FF) and open circuit voltage (V oc) were calculated. The density functional theory was used to optimize the geometries and deduce highest occupied molecular orbital-lowest unoccupied molecular orbital gaps of all the polymers and theoretically predict their optical and electronic properties. Optical properties of all the polymers, electrochemical properties, and band gaps were also obtained experimentally and compared with the theoretically predicted values.

Correlation of P300 ERPs with visual stimuli and its application to vigilance detection.

Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. It is an important aspect in industries such as aerospace and nuclear power, which involve tremendous man-machine interaction and where safety of any component/system or environment as a whole is extremely crucial. Many methods for vigilance detection, based on biological and behavioral characteristics, have been proposed in the literature. Nevertheless, the existing methods are associated with high time complexity, unhandy devices and incur huge equipment overhead. This paper aims to pave an alternative solution to the existing techniques using brain computing interface (BCI). EEG device being a non-invasive BCI technique is popular in many applications. In this work, we have utilized P300 component of ERPs of EEG signal for vigilance detection task as it can be detected fast and accurately. Through this work, we aim to establish the correlation between P300 ERP and vigilance. We have performed a number of experiments to substantiate the correctness of our proposal and have also proposed an approach to measure the vigilance level.

Single-Walled Carbon Nanotubes Decorated with Polypyrrole-TiO2 Nanocomposites.

Nanomaterials decorated with polypyrrole were synthesized using two types of oxidants by chemical oxidative polymerization method. The interaction and influence of the addition of single-walled carbon nanotubes (SWCNTs) and titanium dioxide (TiO2) nanoparticles in polypyrrole (PPy) were studied using Fourier transform infrared spectroscopy and Raman spectroscopy. Thermal stability has been observed by using thermogravimetric analysis. Electrochemical properties were calculated by using Cyclic Voltammetry to study comparative analysis between samples. Particle size measurements and morphology were determined by Field emission transmission electron microscopy. All the nanocomposites exhibit better thermal and electrochemical properties than native polymer. The size of the polypyrrole particles were in the range of 50 nm to 60 nm.

Controlled decoration of the surface with macromolecules: polymerization on a self-assembled monolayer (SAM).

Polymer functionalized surfaces are important components of various sensors, solar cells and molecular electronic devices. In this context, the use of self-assembled monolayer (SAM) formation and subsequent reactions on the surface have attracted a lot of interest due to its stability, reliability and excellent control over orientation of functional groups. The chemical reactions to be employed on a SAM must ensure an effective functional group conversion while the reaction conditions must be mild enough to retain the structural integrity. This synthetic constraint has no universal solution; specific strategies such as "graft from", "graft to", "graft through" or "direct" immobilization approaches are employed depending on the nature of the substrate, polymer and its area of applications. We have reviewed current developments in the methodology of immobilization of a polymer in the first part of the article. Special emphasis has been given to the merits and demerits of certain methods. Another issue concerns the utility - demonstrated or perceived - of conjugated or non-conjugated macromolecules anchored on a functionally decorated SAM in the areas of material science and biotechnology. In the last part of the review article, we looked at the collective research efforts towards SAM-based polymer devices and identified major pointers of progress (236 references).