Improving Crop Yield through Increasing Carbon Gain and Reducing Carbon Loss.

Photosynthesis is a process where solar energy is utilized to convert atmospheric CO into carbohydrates, which forms the basis for plant productivity. The increasing demand for food has created a global urge to enhance yield. Earlier, the plant breeding program was targeting the yield and yield-associated traits to enhance the crop yield.

Empirical Mode Decomposition Based Measures for Investigating the Progression of Pregnancy from Uterine EMG.

The objective of this study is to analyze the uterine electromyography (uEMG) signals to study the progression of pregnancy under term condition (gestational age > 36 weeks) using EMD-based time-frequency features. uEMG signals are obtained from the multiple public datasets during two conditions, namely T1 (acquired < 26 gestational weeks) and T2 (acquired ≥ 26 gestational weeks). The considered signals are preprocessed.

Cemento-Ossifying Fibroid Epulis in the Posterior Maxilla.

Cemento-ossifying fibroma is a benign fibro-osseous lesion arising from the periodontal ligament and has the potential to form cementum and bone in the periodontal ligament. Cemento-ossifying fibroma is a painless, pedunculated, or sessile, smooth exophytic growth arising attached to the gingival tissues. We present a case of cemento-ossifying fibroid epulis in the posterior maxilla attached to the interdental gingiva between the 26 and 27 region buccally in a 52-year-old female patient managed with surgical excision of the lesion, extraction of the involved teeth, curettage, and palatal obturator while under general anesthesia.

Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence Among Dentists and Dental Students: A Cross-Sectional Online Survey.

Background: Artificial intelligence (AI) is the process by which it is possible to program computers to mimic human thoughts. AI and its subsets machine learning and deep learning have been developed to analyze complicated data gathered from many sources using algorithms built into decision support systems. It has been widely used in the field of dentistry.

Diversity and impacts of macroalgae and cyanobacteria on multi-stressed coral reefs in the Gulf of Mannar Marine Biosphere Reserve.

In India, intertidal seaweed resources are widely investigated and utilized for various applications, whereas reef-associated seaweed resources and their impacts on corals are lesser known. Thus, the present study investigated the diversity and impacts of macroalgae and cyanobacteria on coral reefs distributed in 21 islands under the Gulf of Mannar Marine Biosphere Reserve (GoMMBR), Tamil Nadu. About 140 macroalgal species representing 53 species of Chlorophyta, 32 species of Ochrophyta (Phaeophyta), and 55 species of Rhodophyta were recorded.

Investigation of the Mechanical Behavior of Acacia-Raffia Natural Fiber Composite.

Nowadays, industries place a strong emphasis on low-cost, biodegradable materials with long lifespans. As a result, businesses are concentrating on creating composite materials utilizing the world's plentiful supply of natural fibers. In this study, acacia and raffia fibers are combined with epoxy resin and a hand layup method to create a biodegradable composite laminate.

Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution.

In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG.

Histopathological Changes in Oral Tissues Induced by Pesticide Poisoning: A Pilot Study.

Objective: The present study evaluated the histopathological changes in oral tissues induced by pesticide poisoning.

Patients And Methods: This was a cross-sectional pilot study. The sample consisted of oral tissues obtained from deceased patients during autopsy.

Detection of Tonic-Clonic Seizures using Wavelet Entropy of Scalp EEG.

Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Clonic seizures (TCSZ) may lead to injuries, and constitute the major risk factor for sudden unexpected death in epilepsy (SUDEP), especially in unattended patients.

Analysis of Facial Electromyography Signals Using Linear and Non-Linear Features for Human-Machine Interface.

In this work, an attempt has been made to analyze the facial electromyography (facial EMG) signals using linear and non-linear features for the human-machine interface. Facial EMG signals are obtained from the publicly available, widely used DEAP dataset. Thirty-two healthy subjects volunteered for the establishment of this dataset.

A checklist of seaweeds of the Andaman and Nicobar Islands, India: a way forward for seaweed cultivation, food, and drug applications.

Seaweed exploitation in mainland India was started in early 1970s for various applications. The checklist of seaweeds of mainland India was well documented during 2001. However, rich marine macroalgal diversity found in island ecosystems of Andaman and Nicobar Islands have not been explored until today due to inaccessibility to most remote and protected islands.

Analysis of Frequency Bands of Uterine Electromyography Signals for the Detection of Preterm Birth.

In this work, an attempt has been made to analyze the influence of the frequencies bands in uterine electromyography (uEMG) signals on the detection of preterm birth. The signals recorded from the women's abdomen during pregnancy are considered in this study. The signals are subjected to preprocessing using digital bandpass Butterworth filter and decomposed into different frequency bands namely, 0.

Analysis of EEG-EMG Coherence in Low Frequency Bands.

In this, study, an attempt is made to analyze the corticomuscular coupling of the brain and muscular system in the low-frequency components during ramp descent (RD) and stair descent (SD) locomotion. For this purpose, magnitude squared coherence (MSC) is computed from the simultaneous EEG and EMG signals recorded during the ramp and stair descent tasks. The MSC is extracted from the low- frequency bands such as delta (0.

Emotion Recognition Using Spectral Feature from Facial Electromygraphy Signals for Human-Machine Interface.

Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset.

Fluorescence and quantum mechanical approach on the interaction of amides and their role on the stability and coexistence of the rotamer conformations of L-tryptophan in aqueous solution.

Photophysical investigation on the fluorescence decay characteristics of L-tryptophan and a derivative N-acetyl-L-tryptophanamide (NATA) with alkyl amides were carried out in water. L-tryptophan exists in the zwitterionic form and exhibits a biexponential lifetime which is correlated to the existence of rotamer structures. Addition of formamide (F) and dimethylformamide (DMF) results in a decrease in the fluorescence lifetime and its proportion of the most stable structure of L-tryptophan wherein acetamide (ACM) results in an increase of the same.

Microbial diversity from the continental shelf regions of the Eastern Arabian Sea: A metagenomic approach.

The marine microbiome is a complex and least-understood habitat, which play a significant role in global biogeochemical cycles. The present study reported the culture-independent assessment of microbial diversity from the Arabian Sea (AS) sediments (from Gujarat to Malabar; at 30 m depth) by using metagenome sequence analysis. Our results elucidated that bacterial communities in the Malabar coastal region are highly diverse than the Gujarat coast.

Antimicrobial compounds produced by Lysinibacillus odysseyi epiphytic bacteria associated with red algae.

Seaweed epiphytic bacteria are highly host specific and their association is little known. The marine environment of Andaman Islands is rich in algal diversity and their association with microbial communities remains unexplored. We investigated the epiphytic bacterial communities from the intertidal red alga Gracilaria canaliculata.

Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson's disease using microelectrode recordings and wavelet packet features.

Background: Deep brain stimulation (DBS) to the subthalamic nucleus (STN) is an effective neurosurgery that overcomes the motor system alternations of patients with advanced Parkinson's disease. The most challenging aspect of DBS surgery is the accurate identification of STN and its borders. In general, it is performed manually by a neurophysiologist using the microelectrode recordings (MERs).

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson's disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature.

Could we have missed out the seizure onset: A study based on intracranial EEG.

Objective: Intracranial EEG covers only a small fraction of brain volume and it is uncertain if a discharge represents a true seizure onset or results from spread. We therefore assessed if there are differences between characteristics of the ictal onset when we are likely to have a true onset, and characteristics of the discharge in regions of spread.

Methods: Wavelet based statistical features were extracted in 503 onset and 390 spread channels of 58 seizures from 20 patients.

Antimicrobial Potential of Epiphytic Bacteria Associated With Seaweeds of Little Andaman, India.

Seaweeds of the intertidal regions are a rich source of surface associated bacteria and are potential source of antimicrobial molecules. In the present study, 77 epiphytic isolates from eight different algae collected from Little Andaman were enumerated. On testing for their antimicrobial activities against certain pathogens twelve isolates showed positive and six of them showed significant antimicrobial inhibition zone against type 1, type 2a, type 5, Enterotoxigenic O115, Enteropathogenic serotype O114, ; O1 Ogawa Based on the activity these six isolates (G1C, G2C, G3C, UK, UVAD, and Tor1) were identified by 16S rRNA gene sequence and were found to belong to the phyla Firmicutes and Proteobacteria.

Prediction of secondary generalization from a focal onset seizure in intracerebral EEG.

Objective: We propose a system based on the first five seconds of intracerebrally recorded focal seizures to predict their evolution from focal to bilateral tonic-clonic seizure (FTC), to spread outside the onset zone but without tonic-clonic component (FS), or to a seizure remaining focal (F).

Methods: Nineteen time and frequency domain features were extracted from 39 seizures of 32 patients and were subjected to support vector machine based classification. Three prediction approaches regarding seizure evolution were (1) FTC vs FS vs F, (2) FTC vs FS or F and (3) FTC or FS vs F.

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

Background And Objective: Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals.

Analysis of biceps brachii sEMG signal using Multiscale Fuzzy Approximate Entropy.

In this work, an attempt has been made to differentiate sEMG signals under muscle fatigue and non-fatigue conditions using multiscale features. Signals are recorded from biceps brachii muscle of 50 normal adults during repetitive dynamic contractions. After preprocessing, the signal is divided into six segments, out of which first and last segments are considered for this analysis.