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Cyrtostachys renda (CR) and Ptychosperma macarthurii (PM) fibers, as new cellulosic fibers were studied. The objective of this research is to evaluate the fibers from stem, leaf stalk, and frond of these two plants as reinforcement in the polymer composites. The density, chemical composition, functional group, crystallinity index, and thermal stability were studied for CR and PM fibers. From the analysis, the density and thermal stability of both leaf stalk fibers are found almost similar. The highest crystallinity index (55.67%) of the leaf stalk of CR was attributed to the highest cellulosic content (38.99%) and the functional group (C-H stretching). From the results, the optimized properties were found for the leaf stalk of CR fiber. The tensile strength, interfacial shear strength (IFSS), morphology of the leaf stalk of CR were explored. The ultimate tensile strength, Young modulus, and IFSS of the leaf stalk of CR is 51.82 ± 9.41 MPa, 0.69 ± 0.18 GPa, and 3.27 ± 0.5 MPa, respectively. The Scanning Electron Microscopy observation revealed that the leaf stalk fiber of CR possesses anti-microbial properties which has been corroborated through the presence of silica bodies over the fiber surface. Hence, the leaf stalk of CR acts as a potential reinforcement in the polymeric composites for lightweight applications.

Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR.

Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.

Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts’ nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults.

Syringic acid (SRA) is an excellent anti-oxidant and anti-cancer property in various in vitro and in vivo studies. In the present study was modifying effect of SRA on 7,12-dimethylbenz(a)anthracene (DMBA) induced cell surface glycoconjugates (GCs) abnormalities in the plasma and buccal mucosa of golden Syrian hamster buccal pouch carcinogenesis (HBPCs). Topical application of DMBA three times a week for 10 weeks on the buccal pouches of the hamsters resulted in well developed squamous cell carcinoma. GCs status was assessed biochemically, histological and immunoexpression pattern of cytokeratin (CK) in the buccal mucosa of the DMBA treated hamsters. Elevated levels of GCs and CK expression were observed in DMBA alone treated hamsters. Oral pre-administration of SRA (50 mg/kg bw) positively modulates the GCs levels and CK expressions to near normal. The present findings suggested that SRA can protect cell surface GCs and CK expression during DMBA induced HBPCs. Keywords: Syringic acid, Glycoconjugates, 7,12-dimethylbenz(a)anthracene, Oral cancer, Cytokeratin

BackgroundCOVID-19 forced healthcare systems to make unprecedented changes in clinical care processes. The authors hypothesized that the COVID-19 pandemic adversely impacted timely access to care, perioperative processes, and clinical outcomes for pediatric patients undergoing primary appendectomy.MethodsA retrospective, international, multicenter study was conducted using matched cohorts within participating centers of the international PEdiatric Anesthesia COVID-19 Collaborative (PEACOC). Patients younger than 18 yr old were matched using age, American Society of Anesthesiologists Physical Status, and sex. The primary outcome was the difference in hospital length of stay of patients undergoing primary appendectomy during a 2-month period early in the COVID-19 pandemic (April to May 2020) compared with prepandemic (April to May 2019). Secondary outcomes included time to appendectomy and the incidence of complicated appendicitis.ResultsA total of 3,351 cases from 28 institutions were available with 1,684 cases in the prepandemic cohort matched to 1,618 in the pandemic cohort. Hospital length of stay was statistically significantly different between the two groups: 29 h (interquartile range: 18 to 79) in the pandemic cohort versus 28 h (interquartile range: 18 to 67) in the prepandemic cohort (adjusted coefficient, 1 [95% CI, 0.39 to 1.61]; P < 0.001), but this difference was small. Eight centers demonstrated a statistically significantly longer hospital length of stay in the pandemic period than in the prepandemic period, while 13 were shorter and 7 did not observe a statistically significant difference. During the pandemic period, there was a greater occurrence of complicated appendicitis, prepandemic 313 (18.6%) versus pandemic 389 (24.1%), an absolute difference of 5.5% (adjusted odds ratio, 1.32 [95% CI, 1.1 to 1.59]; P = 0.003). Preoperative SARS-CoV-2 testing was associated with significantly longer time-to-appendectomy, 720 min (interquartile range: 430 to 1,112) with testing versus 414 min (interquartile range: 231 to 770) without testing, adjusted coefficient, 306 min (95% CI, 241 to 371; P < 0.001), and longer hospital length of stay, 31 h (interquartile range: 20 to 83) with testing versus 24 h (interquartile range: 14 to 68) without testing, adjusted coefficient, 7.0 (95% CI, 2.7 to 11.3; P = 0.002).ConclusionsFor children undergoing appendectomy, the COVID-19 pandemic did not significantly impact hospital length of stay.Editor’s perspective

Inherently low concentrations of zinc (Zn), iron (Fe), iodine (I), and selenium (Se) in wheat (Triticum aestivum L.) grains represent a major cause of micronutrient malnutrition (hidden hunger) in human populations. Genetic biofortification represents a highly useful solution to this problem. However, genetic biofortification alone may not achieve desirable concentrations of micronutrients for human nutrition due to several soil- and plant-related factors. This study investigated the response of genetically biofortified high-Zn wheat genotypes to soil-applied Zn and foliarly applied Zn, I, and Se in India and Pakistan. The effect of soil-applied Zn (at the rate of 50 kg ha−1 as ZnSO4·7H2O) and foliar-applied Zn (0.5% ZnSO4·7H2O), I (0.04% KIO3), Se (0.001% Na2SeO4), and a foliar cocktail (F-CT: combination of the above foliar solutions) on the grain concentrations of Zn, I, Se, and Fe of high-Zn wheat genotypes was investigated in field experiments over 2 years. The predominantly grown local wheat cultivars in both countries were also included as check cultivars. Wheat grain yield was not influenced by the micronutrient treatments at all field locations, except one location in Pakistan where F-CT resulted in increased grain yield. Foliar-applied Zn, I, and Se each significantly enhanced the grain concentration of the respective micronutrients. Combined application of these micronutrients was almost equally effective in enhancing grain Zn, I, and Se, but with a slight reduction in grain yield. Foliar-applied Zn, Zn+I, and F-CT also enhanced grain Fe. In India, high-Zn genotypes exhibited a minor grain yield penalty as compared with the local cultivar, whereas in Pakistan, high-Zn wheat genotypes could not produce grain yield higher than the local cultivar. The study demonstrates that there is a synergism between genetic and agronomic biofortification in enrichment of grains with micronutrients. Foliar Zn spray to Zn-biofortified genotypes provided additional increments in grain Zn of more than 15 mg kg−1. Thus, combining agronomic and genetic strategies will raise grain Zn over 50 mg kg−1. A combination of fertilization practice with plant breeding is strongly recommended to maximize accumulation of micronutrients in food crops and to make significant progress toward resolving the hidden hunger problem in human populations.

In this millennium, non-wood plants have emerged as a substitute for fibrous substantial attributing to growth in population, industrialization and education. Natural fiber composites are crucial for creating high-performance engineering resources because of their easy accessibility, capacity to be recycled and environmental friendliness. Products from the palm are important and cost-effective for both civic and pastoral locations. The palm fruit cluster, leaf trunks and leaves are utilized domestically and parts are thrown away as waste. This palm waste product is used as a raw substantial for reinforcement in polymer composites as well as a possible source for natural fibers. An overview of the advancements made in the field of Arecaceae (palm) family-based fiber-reinforced composites is presented including information on their current market, matrix combinations, physical qualities, environmental implications, manufacturing methods, chemical processing and acoustic features. In order to optimize the utilization of this bio-based substance and ensure its bright future, a number of important challenges are explored along with ideas for further investigation. The discussion of the impact of palm fibers on final products and the literature analysis highlight additive manufacturing and machine learning-based optimization methods for natural fiber composites (NFC).

The concept of the energy of a graph has been widely explored in the field of mathematical chemistry and is defined as the sum of the absolute values of the eigenvalues of its adjacency matrix. The energy of a hypergraph is the trace norm of its connectivity matrices, which generalize the concept of graph energy. In this paper, we establish bounds for the adjacency energy of hypergraphs in terms of the number of vertices, maximum degree, eigenvalues, and the norm of the adjacency matrix. Additionally, we compute the sum of squares of adjacency eigenvalues of linear k-hypergraphs and derive its bounds for k-hypergraph in terms of number of vertices and uniformity of the k-hypergraph. Moreover, we determine the Nordhaus–Gaddum type bounds for the adjacency energy of k-hypergraphs.