Mechanical Fourier transform for programmable metamaterials.

Proactively programming materials toward target nonlinear mechanical behaviors is crucial to realize customizable functions for advanced devices and systems, which arouses persistent explorations for rapid and efficient inverse design strategies. Herein, we propose a "mechanical Fourier transform" strategy to program mechanical behaviors of materials by mimicking the concept of Fourier transform. In this strategy, an arbitrary target force-displacement curve is decomposed into multiple cosine curves and a constant curve, each of which is realized by a rationally designed multistable module in an array-structured metamaterial. Various target curves with distinct shapes can be rapidly programmed and reprogrammed through only amplitude modulation on the modules. Two exemplary metamaterials are demonstrated to validate the strategy with a macroscale prototype based on magnet lattice and a microscale prototype based on an etched silicon wafer. This strategy applies to a variety of scales, constituents, and structures, and paves a way for the property programming of materials.

Coupling and regulation mechanisms of the flavin-dependent halogenase PyrH observed by infrared difference spectroscopy.

Flavin-dependent halogenases are central enzymes in the production of halogenated secondary metabolites in various organisms and they constitute highly promising biocatalysts for regioselective halogenation. The mechanism of these monooxygenases includes formation of hypohalous acid from a reaction of fully reduced flavin with oxygen and halide. The hypohalous acid then diffuses via a tunnel to the substrate-binding site for halogenation of tryptophan and other substrates. Oxidized flavin needs to be reduced for regeneration of the enzyme, which can be performed in vitro by a photoreduction with blue light. Here, we employed this photoreduction to study characteristic structural changes associated with the transition from oxidized to fully reduced flavin in PyrH from Streptomyces rugosporus as a model for tryptophan-5-halogenases. The effect of the presence of bromide and chloride or the absence of any halides on the UV-vis spectrum of the enzyme demonstrated a halide-dependent structure of the flavin-binding pocket. Light-induced FTIR difference spectroscopy was applied and the signals assigned by selective isotope labeling of the protein moiety. The identified structural changes in α-helix and ß-sheet elements were strongly dependent on the presence of bromide, chloride, the substrate tryptophan, and the product 5-chloro-tryptophan, respectively. We identified a clear allosteric coupling in solution at ambient conditions between cofactor-binding site and substrate-binding site that is active in both directions, despite their separation by a tunnel. We suggest that this coupling constitutes a fine-tuned mechanism for the promotion of the enzymatic reaction of flavin-dependent halogenases in dependence of halide and substrate availability.

Nanoscale Infrared Spectroscopic Characterization of Extended Defects in 4H-Silicon Carbide.

Extended defects in wide-bandgap semiconductors have been widely investigated using techniques providing either spectroscopic or microscopic information. Nano-Fourier transform infrared spectroscopy (nano-FTIR) is a nondestructive characterization method combining FTIR with nanoscale spatial resolution (∼20 nm) and topographic information. Here, we demonstrate the capability of nano-FTIR for the characterization of extended defects in semiconductors by investigating an in-grown stacking fault (IGSF) present in a 4H-SiC epitaxial layer. We observe a local spectral shift of the mid-infrared near-field response, consistent with the identification of the defect stacking order as 3C-SiC (cubic) from comparative simulations based on the finite dipole model (FDM). This 3C-SiC IGSF contrasts with the more typical 8H-SiC IGSFs reported previously and is exemplary in showing that nanoscale spectroscopy with nano-FTIR can provide new insights into the properties of extended defects, the understanding of which is crucial for mitigating deleterious effects of such defects in alternative semiconductor materials and devices.

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution.

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

Enhanced extracellular vesicles mediated uttroside B (Utt-B) delivery to Hepatocellular carcinoma cell: Pharmacokinetics based on PBPK modelling.

Our prior investigation has confirmed that the anti-hepatocellular carcinoma activity of the plant saponin, specifically Uttroside B (Utt-B), derived from the leaves of Solanum nigrum Linn. This study concentrated on formulating a novel biocompatible nanocarrier utilizing Extracellular vesicles (EVs) to enhance the delivery of plant saponin into cells. The physicochemical attributes of Extracellular Vesicles/UttrosideB (EVs/Utt-B) were comprehensively characterized through techniques such as Transmission Electron Microscopy (TEM) and Fourier-transform infrared spectroscopy (FTIR). Despite the promising therapeutic potential of this uttroside B, mechanistic know-how about its entry into cells is still in its infancy. Our research sheds light on the extracellular vesicle-mediated mechanism facilitating the entry of the saponin into cells, a phenomenon confirmed through the use of by confocal microscopy. We further analysed drug-releasing kinetics and simulated the Pharmacokinetics by PBPK modelling. The simulated pharmacokinetics revealed the bioavailability of Uttroside-B in oral administration against intravenous administration.

Attenuated total reflection Fourier-transform infrared spectroscopy reveals environment specific phenotypes in clonal Japanese knotweed.

BACKGROUND: Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP. RESULTS: Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters. CONCLUSIONS: Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

Sleep alterations as a function of 88 health indicators.

BACKGROUND: Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health. METHODS: We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications. RESULTS: Of sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects. CONCLUSIONS: Sleep alterations are present in various health conditions.

Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing.

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

New insights on collagen structural organization and spatial distribution around dental implants: a comparison between machined and laser-treated surfaces.

BACKGROUND: One of the main factors for the osseointegration of dental implants is the development of an adequate soft tissue barrier, mainly composed by collagen, which protects the implant from bacterial development. The structural features of the peri-implant collagen are influenced by the implant components and, in particular, by the type of the surface. In the clinical practice, healing abutments are characterized by smooth surfaces, named machined. Recently, a new laser technique, Synthegra, has been developed to obtain a topography-controlled surface with micrometric regular pores that seems reducing the risk of peri-implantitis. Based on this background, this study aims investigating the structural organization and spatial distribution of collagen surrounding healing abutments characterized by laser-treated and machined surfaces. METHODS: Gingiva portions surrounding custom-made healing abutments (HA), characterized by alternated laser-treated and machined surfaces, were collected and analyzed by combining Fourier Transform InfraRed Imaging (FTIRI) spectroscopy, a non-invasive and high-resolution bidimensional analytical technique, with histological and multivariate analyses. RESULTS: Masson's trichrome staining, specific for collagen, highlighted a massive presence of collagen in all the analyzed samples, evidencing a surface-related spatial distribution. The nature of collagen, investigated by the FTIRI spectroscopy, appeared more abundant close to the laser-treated surface, with a perpendicular disposition of the bundles respect to the HA; conversely, a parallel distribution was observed around the machined surface. A different secondary structure was also found, with a higher amount of triple helices and a lower quantity of random coils in collagen close to the laser treated surfaces. CONCLUSIONS: FTIRI spectroscopy demonstrates that the use of a laser treated transmucosal surface can improve the morphological organization of the peri-implant collagen, which presents a distribution more similar to that of natural teeth. TRIAL REGISTRATION: This trial is registered with ClinicalTrials.gov Identifier: (Registration Number: NCT05754970). Registered 06/03/2023, retrospectively registered, https://clinicaltrials.gov/show/NCT05754970 .

Advances in high-resolution mass spectrometry techniques for analysis of high mass-to-charge ions.

A major challenge in modern mass spectrometry (MS) is achieving high mass resolving power and accuracy for precision analyses in high mass-to-charge (m/z) regions. To advance the capability of MS for increasingly demanding applications, understanding limitations of state-of-the-art techniques and their status in applied sciences is essential. This review summarizes important instruments in high-resolution mass spectrometry (HRMS) and related advances to extend their working range to high m/z regions. It starts with an overview of HRMS techniques that provide adequate performance for macromolecular analysis, including Fourier-transform, time-of-flight (TOF), quadrupole-TOF, and related data-processing techniques. Methodologies and applications of HRMS for characterizing macromolecules in biochemistry and material sciences are summarized, such as top-down proteomics, native MS, drug discovery, structural virology, and polymer analyses.

How Does the Powder Mixture of Ibuprofen and Caffeine Attenuate the Solubility of Ibuprofen? Comparative Study for the Xanthine Derivatives to Recognize Their Intermolecular Interactions Using Fourier-Transform Infrared (FTIR) Spectra, Differential Scanning Calorimetry (DSC), and X-ray Powder Diffractometry (XRPD).

Molecular interactions between active pharmaceutical ingredients (APIs) and xanthine (XAT) derivatives were analyzed using singular value decomposition (SVD). XAT derivatives were mixed with equimolar amounts of ibuprofen (IBP) and diclofenac (DCF), and their dissolution behaviors were measured using high-performance liquid chromatography. The solubility of IBP decreased in mixtures with caffeine (CFN) and theophylline (TPH), whereas that of DCF increased in mixtures with CFN and TPH. No significant differences were observed between the mixtures of theobromine (TBR) or XAT with IBP and DCF. Mixtures with various molar ratios were analyzed using differential scanning calorimetry, X-ray powder diffraction, and Fourier-transform infrared spectroscopy to further explore these interactions. The results were subjected to SVD. This analysis provides valuable insights into the differences in interaction strength and predicted interaction sites between XAT derivatives and APIs based on the combinations that form mixtures. The results also showed the impact of the XAT derivatives on the dissolution behavior of IBP and DCF. Although IBP and DCF were found to form intermolecular interactions with CFN and TPH, these effects resulted in a reduction of the solubility of IBP and an increase in the solubility of DCF. The current approach has the potential to predict various interactions that may occur in different combinations, thereby contributing to a better understanding of the impact of health supplements on pharmaceuticals.

Microbial degradation of low-density polyethylene, polyethylene terephthalate, and polystyrene by novel isolates from plastic-polluted environment.

Biodegradation is an eco-friendly measure to address plastic pollution. This study screened four bacterial isolates that were capable of degrading recalcitrant polymers, i.e., low-density polyethylene, polyethylene terephthalate, and polystyrene. The unique bacterial isolates were obtained from plastic polluted environment. Dermacoccus sp. MR5 (accession no. OP592184) and Corynebacterium sp. MR10 (accession no. OP536169) from Malaysian mangroves and Bacillus sp. BS5 (accession no. OP536168) and Priestia sp. TL1 (accession no. OP536170) from a sanitary landfill. The four isolates showed a gradual increase in the microbial count and the production of laccase and esterase enzymes after 4 weeks of incubation with the polymers (independent experiment set). Bacillus sp. BS5 produced the highest laccase 15.35 ± 0.19 U/mL and showed the highest weight loss i.e., 4.84 ± 0.6% for PS. Fourier transform infrared spectroscopy analysis confirmed the formation of carbonyl and hydroxyl groups as a result of oxidation reactions by enzymes. Liquid chromatography-mass spectrometry analysis showed the oxidation of the polymers to small molecules (alcohol, ethers, and acids) assimilated by the microbes during the degradation. Field emission scanning electron microscopy showed bacterial colonization, biofilm formation, and surface erosion on the polymer surface. The result provided significant insight into enzyme activities and the potential of isolates to target more than one type of polymer for degradation.

Characterization of an outbreak caused by Elizabethkingia miricola using Fourier-transform infrared (FTIR) spectroscopy.

Fourier-transform infrared (FTIR) spectroscopy has the potential to be used for bacterial typing and outbreak characterization. We evaluated FTIR for the characterization of an outbreak caused by Elizabethkingia miricola. During the 2020-2021 period, 26 isolates (23 clinical and 3 environmental) were collected and analyzed by FTIR (IR Biotyper) and core-genome MLST (cgMLST), in addition to antimicrobial susceptibility testing. FTIR spectroscopy and cgMLST showed that 22 of the isolates were related to the outbreak, including the environmental samples, with only one discordance between both methods. Then, FTIR is useful for E. miricola typing and can be easily implemented in the laboratory.

Elucidating Factors Contributing to Dicamba Volatilization by Characterizing Chemical Speciation in Dried Dicamba-Amine Residues.

Dicamba is a semivolatile herbicide that has caused widespread unintentional damage to vegetation due to its volatilization from genetically engineered dicamba-tolerant crops. Strategies to reduce dicamba volatilization rely on the use of formulations containing amines, which deprotonate dicamba to generate a nonvolatile anion in aqueous solution. Dicamba volatilization in the field is also expected to occur after aqueous spray droplets dry to produce a residue; however, dicamba speciation in this phase is poorly understood. We applied Fourier transform infrared (FTIR) spectroscopy to evaluate dicamba protonation state in dried dicamba-amine residues. We first demonstrated that commercially relevant amines such as diglycolamine (DGA) and n,n-bis(3-aminopropyl)methylamine (BAPMA) fully deprotonated dicamba when applied at an equimolar molar ratio, while dimethylamine (DMA) allowed neutral dicamba to remain detectable, which corresponded to greater dicamba volatilization. Expanding the amines tested, we determined that dicamba speciation in the residues was unrelated to solution-phase amine pKa, but instead was affected by other amine characteristics (i.e., number of hydrogen bonding sites) that also correlated with greater dicamba volatilization. Finally, we characterized dicamba-amine residues containing an additional component (i.e., the herbicide S-metolachlor registered for use alongside dicamba) to investigate dicamba speciation in a more complex chemical environment encountered in field applications.

Direct Analysis of Marine Dissolved Organic Matter Using LC-FT-ICR MS.

Marine dissolved organic matter (DOM) is an important component of the global carbon cycle, yet its intricate composition and the sea salt matrix pose major challenges for chemical analysis. We introduce a direct injection, reversed-phase liquid chromatography ultrahigh resolution mass spectrometry approach to analyze marine DOM without the need for solid-phase extraction. Effective separation of salt and DOM is achieved with a large chromatographic column and an extended isocratic aqueous step. Postcolumn dilution of the sample flow with buffer-free solvents and implementing a counter gradient reduced salt buildup in the ion source and resulted in excellent repeatability. With this method, over 5,500 unique molecular formulas were detected from just 5.5 nmol carbon in 100 µL of filtered Arctic Ocean seawater. We observed a highly linear detector response for variable sample carbon concentrations and a high robustness against the salt matrix. Compared to solid-phase extracted DOM, our direct injection method demonstrated superior sensitivity for heteroatom-containing DOM. The direct analysis of seawater offers fast and simple sample preparation and avoids fractionation introduced by extraction. The method facilitates studies in environments, where only minimal sample volume is available e.g. in marine sediment pore water, ice cores, or permafrost soil solution. The small volume requirement also supports higher spatial (e.g., in soils) or temporal sample resolution (e.g., in culture experiments). Chromatographic separation adds further chemical information to molecular formulas, enhancing our understanding of marine biogeochemistry, chemodiversity, and ecological processes.

Towards a universal method for middle-down analysis of antibodies via proton transfer charge reduction-Orbitrap mass spectrometry.

Modern mass spectrometry technology allows for extensive sequencing of the ~ 25 kDa subunits of monoclonal antibodies (mAbs) produced by IdeS proteolysis followed by disulfide bond reduction, an approach known as middle-down mass spectrometry (MD MS). However, the spectral congestion of tandem mass spectra of large polypeptides dramatically complicates fragment ion assignment. Here, we report the development and benchmark of an MD MS strategy based on the combination of different ion fragmentation techniques with proton transfer charge reduction (PTCR) to simplify the gas-phase sequencing of mAb subunits. Applied on the liquid chromatography time scale using an Orbitrap Tribrid mass spectrometer, PTCR produces easy-to-interpret mass spectra with limited ion signal overlap. We demonstrate that the accurate estimation of the number of charges submitted to the Orbitrap mass analyzer after PTCR allows for the detection of charge-reduced product ions over a wide mass-over-charge (m/z) window with low parts per million m/z accuracy. Therefore, PTCR-based MD MS analysis increases not only sequence coverage, number of uniquely identified fragments, and number of assigned complementary ion pairs, but also the general confidence in the assignment of subunit fragments. This data acquisition method can be readily applied to any class of mAbs without an apparent need for optimization, and benefits from the high resolving power of the Orbitrap mass analyzer to return sequence coverage of individual subunits exceeding 80% in a single run, and > 90% when just two experiments are combined.

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets. METHODS: We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database. RESULTS: Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels. CONCLUSIONS: Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

Determination of hazardous vapors from the thermal decomposition of organochlorinated silica xerogels with adsorptive properties.

The incorporation of organic groups into sol-gel silica materials is known to have a noticeable impact on the properties and structure of the resulting xerogels due to the combination of the properties inherent to the organic fragments (functionality and flexibility) with the mechanical and structural stability of the inorganic matrix. However, the reduction of the inorganic content in the materials could be detrimental to their thermal stability properties, limiting the range of their potential applications. Therefore, this work aims to evaluate the thermal stability of hybrid inorganic-organic silica xerogels prepared from mixtures of tetraethoxysilane and organochlorinated triethoxysilane precursors. To this end, a series of four materials with a molar percentage of organochlorinated precursor fixed at 10%, but differing in the type of organic group (chloroalkyls varying in the alkyl-chain length and chlorophenyl), has been selected as model case study. The gases and vapors released during the thermal decomposition of the samples under N2 atmosphere have been analyzed and their components determined and quantified using a thermogravimetric analyzer coupled to a Fourier-transform infrared spectrophotometer and to a gas chromatography-mass spectrometry unit. These analyses have allowed to identify up to three different thermal events for the pyrolysis of the organochlorinated xerogel materials and to elucidate the reaction pathways associated with such processes. These mechanisms have been found to be strongly dependent on the specific nature of the organic group.

Wood biochar induced metal tolerance in Maize (Zea mays L.) plants under heavy metal stress.

Due to metal toxicity, widespread industrialization has negatively impacted crop yield and soil quality. The current study was aimed to prepare and characterize biochar made from wood shavings of Pinus roxburghii and to determine the plant growth promoting and heavy metal detoxification of cadmium (Cd) and chromium (Cr) contaminated soil. FTIR SEM coupled with EDX characterization of biochar was performed; Cd and Cr were used at a rate of 20 mg/kg. Biochar was used at the rate of 50 mg/kg for various treatments. The completely randomized design (CRD) was used for the experiment and three replicates of each treatment were made. Various agronomic and enzymatic parameters were determined. The results indicated that all growth and enzymatic parameters were enhanced by the prepared biochar treatments. The most prominent results were observed in treatment T5 (in which shoot length, root length, peroxidase dismutase (POD), superoxide dismutase (SOD) catalyzes (CAT), and chlorophyll a and b increased by 28%, 23%, 40%, 41%, 42%, and 27%, respectively, compared to the control). This study demonstrated that biochar is a sustainable and cost-effective approach for the remediation of heavy metals, and plays a role in plant growth promotion. Farmers may benefit from the current findings, as prepared biochar is easier to deliver and more affordable than chemical fertilizers. Future research could clarify how to use biochar optimally, applying the minimum amount necessary while maximizing its benefits and increasing yield.

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.