Fast and scalable querying of eukaryotic linear motifs with gget elm.

MOTIVATION: Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. RESULTS: To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. AVAILABILITY AND IMPLEMENTATION: The manual and source code are available at https://github.com/pachterlab/gget.

Genome-wide association mapping for field spot blotch resistance in South Asian spring wheat genotypes.

Spot blotch caused by Bipolaris sorokiniana ((Sacc.) Shoemaker) (teleomorph: Cochliobolus sativus [Ito and Kuribayashi] Drechsler ex Dastur) is an economically important disease of warm and humid regions. The present study focused on identifying resistant genotypes and single-nucleotide polymorphism (SNP) markers associated with spot blotch resistance in a panel of 174 bread spring wheat lines using field screening and genome-wide association mapping strategies. Field experiments were conducted in Agua Fria, Mexico, during the 2019-2020 and 2020-2021 cropping seasons. A wide range of phenotypic variation was observed among genotypes tested during both years. Twenty SNP markers showed significant association with spot blotch resistance on 15 chromosomes, namely, 1A, 1B, 2A, 2B, 2D, 3A, 3B, 4B, 4D, 5A, 5B, 6A, 6B, 7A, and 7B. Of these, two consistently significant SNPs on 5A, TA003225-0566 and TA003225-1427, may represent a new resistance quantitative trait loci. Further, in the proximity of Tsn1 on 5B, AX-94435238 was the most stable and consistent in both years. The identified genomic regions could be deployed to develop spot blotch-resistant genotypes, particularly in the spot blotch-vulnerable wheat growing areas.

C-H bond activation by high-valent iron/cobalt-oxo complexes: a quantum chemical modeling approach.

High-valent metal-oxo species serve as key intermediates in the activation of inert C-H bonds. Here, we present a comprehensive DFT analysis of the parameters that have been proposed as influencing factors in modeled high-valent metal-oxo mediated C-H activation reactions. Our approach involves utilizing DFT calculations to explore the electronic structures of modeled FeIVO (species 1) and CoIVO ↔ CoIII-O˙ (species 2), scrutinizing their capacity to predict improved catalytic activity. DFT and DLPNO-CCSD(T) calculations predict that the iron-oxo species possesses a triplet as the ground state, while the cobalt-oxo has a doublet as the ground state. Furthermore, we have investigated the mechanistic pathways for the first C-H bond activation, as well as the desaturation of the alkanes. The mechanism was determined to be a two-step process, wherein the first hydrogen atom abstraction (HAA) represents the rate-limiting step, involving the proton-coupled electron transfer (PCET) process. However, we found that the second HAA step is highly exothermic for both species. Our calculations suggest that the iron-oxo species (Fe-O = 1.672 Å) exhibit relatively sluggish behavior compared to the cobalt-oxo species (Co-O = 1.854 Å) in C-H bond activation, attributed to a weak metal-oxygen bond. MO, NBO, and deformation energy analysis reveal the importance of weakening the M-O bond in the cobalt species, thereby reducing the overall barrier to the reaction. This catalyst was found to have a C-H activation barrier relatively smaller than that previously reported in the literature.

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.

Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.

ELM-the Eukaryotic Linear Motif resource-2024 update.

Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.

Fabrication of Unique Mixed-Valent CoICoII and CuICuII Metal-Organic Frameworks (MOFs) for Desulfurization of Fuels: A Combined Experimental and Theoretical Approach toward Green Fuel.

Herein, metal-organic framework (MOF)-based adsorbents are designed with distinct hard and soft metal building units, namely, [Co2ICoII(PD)2(BP)] (Co_PD-BP) and [Cu2ICuII(PD)2(BP)] (Cu_PD-BP), where H2PD = pyrazine-1,4-diide-2,3-dicarboxylic acid and BP = 4,4'-bipyridine. The designed MOFs were characterized via spectral and SCXRD techniques, which confirm the mixed-valent states (+1 and +2) of the metal ions. Topological analysis revealed the rare ths and gwg topologies for Co MOF, while Cu-MOF exhibits a unique 8T21 topology in the 8-c net (point symbol for net: {424·64}). Moreover, severe environmental issues can be resolved by effectively removing heterocyclic organosulfur compounds from fuels via adsorptive desulfurization. Further, the developed MOFs were investigated for sulfur removal via adsorptive desulfurization from a model fuel consisting of dibenzothiophene (DBT), benzothiophene (BT), and thiophene (T) in the liquid phase using n-octane as a solvent. The findings revealed that Cu_PD-BP effectively removes the DBT with a removal efficiency of 86% at 300 ppm and an operating temperature of 25 °C, with a recyclability of up to four cycles. The adsorption kinetic analysis showed that the pseudo-first-order model could fit better with the experimental data indicating the physisorption process. Further, the studies revealed that adsorption capacity increased with the increasing initial DBT concentration with a remarkable capacity of 70.5 mg/g, and the adsorption process was well described by the Langmuir isotherm. The plausible reason behind the enhanced removal efficiency shown by Cu_PD-BP as compared to Co_PD-BP could be the soft-soft interactions between soft sulfur and soft Cu metal centers. Interestingly, density functional theory (DFT) studies were done in order to predict the mechanism of binding of thiophenic compounds with Cu_PD-BP, which further ascertained that along with other interactions, the S···π and S···Cu interactions predominate, resulting in a high uptake of DBT as compared to others. In essence, Cu_PD-BP turns out to be a promising adsorbent in the field of fuel desulfurization for the benefit of mankind.

Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal.

Appropriate blood pressure (BP) management through continuous monitoring and rapid diagnosis helps to take preventive care against cardiovascular diseases (CVD). As hypertension is one of the leading causes of CVDs, keeping hypertension under control by a timely screening of subjects becomes lifesaving. This work proposes estimating BP from motion artifact-affected photoplethysmography signals (PPG) by applying signal processing techniques in realtime. This paper proposes a deep neural network-based methodology to accurately classify PPG signals using a Fourier theory-based time-frequency (TF) spectrogram. This work uses the Fourier decomposition method (FDM) to transform a PPG signal into a TF spectrogram. In the proposed work, the last three layers of the pre-trained deep neural network, namely, GoogleNet, DenseNet, and AlexNet, are modified and then used to classify the PPG signal into normotension, pre-hypertension, and hypertension. The proposed framework is trained and tested using the MIMIC-III and PPG-BP databases using five-fold training and testing. Out of the three deep neural networks, the proposed framework with the DenseNet-201 network performs best, with a test accuracy of 96.5%. The proposed work uses FDM to compute the TF spectrogram to accurately separate the motion artifacts and noise components from a noise-corrupted PPG signal. Capturing more frequency components that contain more information from PPG signals makes the deep neural networks extract better and more meaningful features. Thus, training a deep neural network model with clean PPG signal features improves the generalized capability of a BP classification model when tested in realtime.

Assessment of Indian wheat germplasm for Septoria nodorum blotch and tan spot reveals new QTLs conferring resistance along with recessive alleles of Tsn1 and Snn3.

The leaf blight diseases, Septoria nodorum blotch (SNB), and tan spot (TS) are emerging due to changing climatic conditions in the northern parts of India. We screened 296 bread wheat cultivars released in India over the past 20 years for seedling resistance against SNB (three experiments) and TS (two experiments). According to a genome-wide association study, six QTLs on chromosome arms 1BL, 2AS, 5BL, and 6BL were particularly significant for SNB across all three years, of which Q.CIM.snb.1BL, Q.CIM.snb.2AS1, Q.CIM.snb.2AS.2, and Q.CIM.snb.6BL appeared novel. In contrast, those on 5BS and 5BL may correspond to Snn3 and Tsn1, respectively. The allelic combination of tsn1/snn3 conferred resistance to SNB, whereas that of Tsn1/Snn3 conferred high susceptibility. As for TS, Tsn1 was the only stably significant locus identified in this panel. Several varieties like PBW 771, DBW 277, and HD 3319, were identified as highly resistant to both diseases that can be used in future wheat improvement programs as resistant donors.

Photophysical properties of four-membered BN3 heterocyclic compounds: theoretical insights.

CONTEXT: Understanding the photochemistry of boron nitrogen (BN)-containing compounds is an important aspect to enhance the various optical and electronic applications. In this work, we have explored the structure, bonding, reactivity, electronic absorption (UV-Vis), and light harvesting efficiency (LHE) of a series of BN3 ring and open-chain systems. The frontier molecular orbitals (FMO) analysis found that ring systems have a low HOMO-LUMO energy gap as compared to the open-chain systems which insinuates the feasibility of ring systems in the optoelectronic materials. Also, the molecular electrostatic potential (MEP) maps have been computed to pursue the electrophilic and nucleophilic sites available at the surface of the compound. Interestingly, we have found that the open-chain compounds show more molecular charge distribution range rather than the ring compounds. The investigation of photophysical properties showed that the UV-Vis absorption significantly red-shifted in BN3 ring systems as compared to open-chain counterparts. Furthermore, light harvesting efficiency (LHE) was also found higher in the ring systems as compared to the BN3 open-chain systems. Moreover, the computed structural parameters are found well corroborated with the available X-ray data. METHODS: Structures of all compounds were optimized by using density functional theory (DFT) method, with M06-2X/6-31G(d,p) level. All the calculations in this work are carried out in Gaussian 16 program package. GaussView6.1 software was used for the modeling of initial geometries and for the plotting of MEP plots. To account the solvent effect on geometries the polarized continuum model (PCM) was used and tetrahydrofuran (THF) taken as solvent. The NBO6.0 program (incorporated in G16 software) was used for the exploration of bonding nature and stabilization energies of B-N bond. The absorption spectra were simulated by using ORCA 4.2 program.

Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework.

The markers that help to predict th function of a cardiovascular system are hemodynamic parameters like blood pressure (BP), stroke volume, heart rate, and cardiac output. Continuous analysis of hemodynamic parameters such as BP can detect abnormalities earlier, preventing cardiovascular diseases (CVDs). However, sometimes due to motion artifacts, it becomes difficult to monitor the BP accurately and classify it. This work presents an optimized deep learning model having the capability to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP) and classify the BP stages simultaneously from the same network using only a single channel photoplethysmography (PPG) signal. The proposed model is designed by exploiting the deep learning framework of a convolutional neural network (CNN), exhibiting the inherent ability to extract features automatically. Moreover, the proposed framework utilizes the superlet transform method to transform a 1-D PPG signal into a 2-D super-resolution time-frequency (TF) spectrogram. A superlet transform separates the peaks related to true PPG signal components and motion artifacts components. Thus, the superlet provides a robust realtime approach to accurately estimating and classifying BP using a single PPG sensor signal and does not require additional ECG and PPG sensor signals for reference. Using a super-resolution spectrogram and CNN model makes the method profitable in motion artifact removal, feature selection, and extraction. Hence the proposed framework becomes less complex for deployment on wearable devices having limited battery resources. The performance of the proposed framework is demonstrated on the publicly available larger dataset MIMIC-III. This work obtained a mean absolute error (MAE) of 2.71 mmHg and 2.42 mmHg for SBP and DBP, respectively. The classification accuracy for the SBP prediction is about 96.79%, whereas it is 98.94% for DBP. From a motion artifact-affected PPG signal, SBP and DBP are estimated. Then the estimated BP is classified into three categories: normotension, prehypertension, and hypertension, and is compared with the state of art methods to show the effectiveness of the proposed optimized framework.

Theoretical study of the formation of metal-oxo species of the first transition series with the ligand 14-TMC: driving factors of the "Oxo Wall".

Terminal metal-oxo species of the early transition metal series are well known, whereas those for the late transition series are rare, and this is related to the "Oxo Wall". Here, we have undertaken a theoretical study on the formation of metal-oxo species from the metal hydroperoxo species of the 3d series (Cr, Mn, Fe, Co, Ni, and Cu) with the ligand 14-TMC (1,4,8,11-tetramethyl-1,4,8,11-tetraazacyclotetradecane) via O⋯O bond cleavage. DFT calculations reveal that the barrier for O⋯O bond cleavage is higher with the late transition metals (Co, Ni, and Cu) than the early transition metals (Cr, Mn, and Fe), and the formed late metal-oxo species are also thermodynamically less stable. The higher barrier may be due to electronic repulsion because of the pairing of d electrons. In the late transition metal series, the electron goes into an antibonding orbital, which decreases the bond order and hence decreases the possibility of metal-oxo formation. Computed structural parameters and spin densities suggest that valence tautomerism occurs in the late transition metal-oxo species which remain as a metal-oxyl. Our findings support the concept of the "Oxo Wall".

A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography.

CONTEXT: Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a PPG signal to monitor and classify BP continuously. Control of BP in realtime is the basis for the prevention of hypertension. PROPOSED APPROACH: This work proposes a CS-NET architecture by unifying CNN and SVM approaches to classify BP using PPG signals. The main objective of the CS-NET method is to establish an accurate and reliable algorithm for the ABP classification. METHODOLOGY: ABP signals are labeled normal and abnormal using the hypertension criteria the American College of Cardiology (ACC)/American Heart Association (AHA) laid down. The proposed CS-NET model incorporates three critical steps in three successive stages. The first stage includes converting a preprocessed PPG signal into a time-frequency (TF) representation called a super-resolution spectrogram by superlet transform. The second stage uses a convolutional neural network (CNN) model with several hidden layers to extract morphological features from every PPG super-resolution spectrogram. The third stage uses a support vector machine (SVM) classifier to classify the PPG signal. RESULTS: PPG signals are used to train and test the proposed model. The performance of the proposed CS-NET method is tested using MIMIC-II, MIMIC-III, and PPG-BP-figshare database in terms of accuracy and F1 score. Moreover, the CS-NET method achieves better results with high accuracy when compared with other benchmark approaches that require an electrocardiogram signal for reference. CONCLUSIONS: The proposed model achieved an aggregate classification accuracy of 98.21% across a five-fold cross-validation technique, making it a reliable approach for BP classification in clinical settings and realtime monitoring.

Non-micellar ganglioside GM1 induces an instantaneous conformational change in Aß42 leading to the modulation of the peptide amyloid-fibril pathway.

Alzheimer's disease is a progressive degenerative condition that mainly affects cognition and memory. Recently, distinct clinical and neuropathological phenotypes have been identified in AD. Studies revealed that structural variation in Aß fibrillar aggregates correlates with distinct disease phenotypes. Moreover, environmental surroundings, including other biomolecules such as proteins and lipids, have been shown to interact and modulate Aß aggregation. Model membranes containing ganglioside (GM1) clusters are specifically known to promote Aß fibrillogenesis. This study unravels the modulatory effect of non-micellar GM1, a glycosphingolipid frequently released from the damaged neuronal membranes, on Aß42 amyloid fibril formation. Using far-UV circular dichroism experiments, we observed a change in the peptide secondary structure from random-coil to ß-turn structures with subsequent generation of predominantly ß-sheet-rich species upon interaction with GM1. Thioflavin-T (ThT) fluorescence assays further indicated that GM1 likely interacts with an amyloidogenic Aß42 intermediate species leading to a possible formation of GM1-modified Aß42 fibril. Statistically, no significant difference in toxicity to RA-differentiated SH-SY5Y cells was observed between Aß42 fibrils and GM1-tweaked Aß42 aggregates. Moreover, GM1-modified Aß42 aggregates exhibited prion-like properties in catalyzing the amyloid fibril formation of both major isomers of Aß, Aß40, and Aß42.

Automatic seizure detection and classification using super-resolution superlet transform and deep neural network -A preprocessing-less method.

CONTEXT: Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in temporary changes in behavior, level of consciousness, and involuntary movements. An accurate prediction of seizures can improve the standard of living in epileptic subjects. The increasing capabilities of machine learning and computer-assisted devices can detect seizures accurately with minimal human intervention. PROPOSED APPROACH: This paper proposes a method to detect seizure and non-seizure events using superlet transform (SLT) and a deep convolution neural network: VGG-19. The electroencephalogram (EEG) dataset from the University of Bonn is used to validate the efficacy of the proposed method. METHODOLOGY: SLT, a high-resolution time-frequency technique, converts EEG records into two-dimensional (2-D) images. SLT provides a high-resolution time-frequency representation reflecting the oscillation bursts in an EEG record. The time-frequency representations as 2-D images are fed to a pre-trained convolutional neural network: VGG-19. The last layers of VGG-19 are replaced with new layers to accommodate the different classification problems. RESULTS: The proposed method achieved an accuracy of 100% for all seven seizure and non-seizure detection cases considered in this work. In the case of three and five-class classification problems, the proposed method has better accuracy than other existing methods. The CHB-MIT scalp EEG database is also used to assess the effectiveness of the proposed method, which achieved a classification accuracy of 94.3% in distinguishing between seizure and non-seizure events. CONCLUSION: The results obtained using the proposed methodology show the efficacy of the proposed method in accurately detecting seizures and other brain activity with the least pre-processing and human involvement. The proposed method can assist medical practitioners by saving their effort and time.

Ganglioside GM1 produces stable, short, and cytotoxic Aß40 protofibrils.

Monosialoganglioside GM1-bound amyloid ß-peptides have been found in patients' brains exhibiting early pathological changes of Alzheimer's disease. Herein, we report the ability of non-micellar GM1 to modulate Aß40 aggregation resulting in the formation of stable, short, rod-like, and cytotoxic Aß40 protofibrils with the ability to potentiate both Aß40 and Aß42 aggregation.

Amyloid fibril formation, structure and domain swapping of acyl-coenzyme A thioesterase-7.

Acyl-coenzyme A thioesterase (Acot) enzymes are involved in a broad range of essential intracellular roles including cell signalling, lipid metabolism, inflammation and the opening of ion channels. Dysregulation in lipid metabolism has been linked to neuroinflammatory and neurological disorders such as Alzheimer's and Parkinson's diseases. Structurally, Acot enzymes adopt a circularised trimeric arrangement with each monomer containing an N- and a C-terminal hotdog domain. Acot7 spontaneously forms amyloid fibrils in vitro under physiological conditions. The resultant amyloid fibrillar structures were characterised by dye-binding fluorescence assays, far-UV circular dichroism spectroscopy, transmission electron microscopy and X-ray fibre diffraction. Acot7 has an unusual mechanism of aggregation with no lag phase. The initial phase (~ 18 h) of aggregation involves conformational rearrangement within the oligomers to form species of enhanced ß-sheet character. The subsequent loss of α-helical structure is accompanied by large-scale amyloid fibril formation. The crystal structure of Acot7 revealed an unexpected arrangement of the two domains within the circularised trimeric structure, which is the basis for a proposed mechanism of amyloid fibril formation involving domain swapping during the initial phase of aggregation. Acot7 formed fibrils in the presence of its substrate arachidonoyl-CoA and its inhibitors and maintained its enzyme activity during fibril assembly. It is proposed that the Acot7 fibrillar form acts as functional amyloid.

Spring Wheat's Ability to Utilize Nitrogen More Effectively Is Influenced by Root Phene Variation.

Genetic improvement for nitrogen use efficiency (NUE) can play a very crucial role in sustainable agriculture. Root traits have hardly been explored in major wheat breeding programs, more so in spring germplasm, largely because of the difficulty in their scoring. A total of 175 advanced/improved Indian spring wheat genotypes were screened for root traits and nitrogen uptake and nitrogen utilization at varying nitrogen levels in hydroponic conditions to dissect the complex NUE trait into its component traits and to study the extent of variability that exists for those traits in Indian germplasm. Analysis of genetic variance showed a considerable amount of genetic variability for nitrogen uptake efficiency (NUpE), nitrogen utilization efficiency (NUtE), and most of the root and shoot traits. Improved spring wheat breeding lines were found to have very large variability for maximum root length (MRL) and root dry weights (RDW) with strong genetic advance. In contrast to high nitrogen (HN), a low nitrogen (LN) environment was more effective in differentiating wheat genotypes for NUE and its component traits. Shoot dry weight (SDW), RDW, MRL, and NUpE were found to have a strong association with NUE. Further study revealed the role of root surface area (RSA) and total root length (TRL) in RDW formation as well as in nitrogen uptake and therefore can be targeted for selection to further the genetic gain for grain yield under high input or sustainable agriculture under limited inputs.

Investigation of efficient photoconduction and enhanced luminescence characteristics of Ce-doped ZnO nanophosphors for UV sensors.

This study involves the single-step, mass-scale productive synthesis, photoconduction, and luminescence characteristics of pure and cerium rare-earth-ion-doped ZnO (CZO) nanophosphors with different Ce concentrations (Ce: 0, 2, 4, 6, and 8 wt.%) synthesized using the solid-state reaction method. The synthesized nanophosphors were characterized for their structural, morphological, optical, and photoconductivity (PC) properties using X-ray diffraction (XRD), field-effect scanning electron microscopy (FE-SEM), energy dispersive spectroscopy, Fourier-transform infrared (FT-IR), photoluminescence (PL), and PC measurements. The sharp diffraction peaks of XRD results exhibit the formation of crystalline hexagonal wurtzite ZnO nanostructures. The decrease in diffraction peak intensities of CZO with an increase in Ce concentrations signifies the deterioration of the ZnO crystal. FE-SEM images exhibit the good crystalline quality of nanophosphors composed of spherical- and elongated-shaped nanoparticles that are distributed consistently on the surface. The energy dispersive X-ray pattern of the 4 wt.% Ce-doped ZnO (CZO4 ) sample confirms the doping of Ce in ZnO. The presence of chemical bonds and functional groups corresponds to transmittance peaks established using FT-IR spectroscopy. Deconvoluted PL spectra show two major emission peaks, one in the UV region, which is near-band-edge, and the other in the visible region ranging from ~456 to 561 nm. In PC studies, current-voltage (I-V) and current-time (I-T) characteristics, that is, rise/decayin current under dark as well as UV light conditions, are also investigated. Efficient photoconduction is observed in CZO samples. The obtained results indicate the suitability to luminescent and photosensor applications.

Analysis of photoplethysmogram signal to estimate heart rate during physical activity using fractional fourier transform - A sampling frequency independent and reference signal-less method.

BACKGROUND AND OBJECTIVE: Acquiring accurate and reliable health information using a PPG signal in wearable devices requires suppressing motion artifacts. This paper presents a method based on the Fractional Fourier transform (FrFT) to effectively suppress the motion artifacts in a Photoplethysmogram (PPG) signal for an accurate estimation of heart rate (HR). METHODS: By analyzing various PPG signals recorded under various physiological conditions and sampling frequencies, the proposed work determines an optimal value of the fractional order of the proposed FrFT. The proposed FrFT-based algorithm separates the motion artifacts component from the acquired PPG signal. Finally, the HR estimation accuracy during the strong motion artifact-affected windows is improved using a post-processing technique. The efficacy of the proposed method is evaluated by computing the root mean square error (RMSE). RESULTS: The performance of the proposed algorithm is compared with methods in recent studies using test and training datasets from the IEEE Signal Processing Cup (SPC). The proposed method provides the mean absolute error of 1.88 beats per minute (BPM) on all twenty-three recordings. CONCLUSIONS: The proposed method uses the Fourier method in the fractional domain. A noisy signal is rotated into an intermediate plane between the time and frequency domains to separate the signal from the noise. The algorithm incorporates FrFT analysis to suppress motion artifacts from PPG signals to estimate HR accurately. Further, a post-processing step is used to track the HR for accurate and reliable HR estimation. The proposed FrFT-based algorithm doesn't require additional reference accelerometers or hardware to estimate HR in real-time. The noise and signal separation is optimum for a fractional order (a) value in the vicinity of 0.6. The optimized value of fractional order is constant irrespective of the physical activity and sampling frequency.

Electronic, geometrical and photophysical facets of five coordinated porphyrin N-heterocyclic carbene transition metals complexes: A theoretical study.

In the realm of dye sensitized solar cells (DSSCs), the 3d transition metals as photosensitizers are scarcely studied. In the present work, electronic structures, FMO, MEP surfaces, NBO analysis, energetics and photophysical properties of earth abundant metals (Mn, Fe and Co) based metalloporphyrins coordinated with NHC-carbene have been explored by using DFT and TDDFT calculations. According to formation energies and energy decomposition analysis (EDA), the cobalt based metalloporphyrins species are found to be more stable while in contrast manganese based species are predicted as more reactive among all. Also, from the ligation point of view, the TPP (meso-tetraphenylporphyrin) ligand forms more steady and rigid coordination as compare to the TTP (meso-tetratolylporphyrin) ligand. FMO analysis also support these observations. NBO and SNO results support the electronic configurations as well as unveil the controversial bonding pattern of NHCcarbon and metal atom and found that there is σ-bonding present between the metal and the NHCcarbon by the overlapping of sp-hybridized orbitals of carbenecarbon and sp/d hybrid orbital of the metal atom. TDDFT results show that the highest light harvesting efficiency (LHE) of all the studied species is found under the range of 360 nm - 380 nm (λ) and this may due to the presence of longer π-conjugations. In-depth investigation of this work may help to design new robust energy harvesting systems for high energy conversion efficiency based on earth abundance metals. Our results are in well agreement with the available experimental findings.