Composition and rheological properties of flour and dough from genetically modified wheat (Triticum aestivum L.) Hi-Line 111

The main objective of this work was to evaluate the composition, nutritional, physical and rheological properties of wheat flour and dough from genetically modified wheat (Triticum aestivum L.) Hi-Line 111 (GMW) compared to conventional wheat (non-GMW). Analyses were conducted to measure the proximate chemical composition with references to 18 components including total solid, protein, lipids, crude fiber, ash, carbohydrate, minerals, amino acids, and fatty acids. In addition, physical and rheological properties such as water absorption, arrival time, dough development time, stability value, dough weakening value, extensibility of dough, resistance to extension, and ratio of resistance/extensibility were evaluated. The results showed that there were no significant differences between GMW and non-GMW in terms of chemical composition. Results revealed the presence of saturated and unsaturated fatty acids wherein there were no significant differences between GMW and its counterpart in the levels of fatty acids. In addition, there were no significant differences on the levels of amino acids. In addition, there were no significant differences between the GMW and non-GMW in the physical and rheological properties. From these results, it can be concluded that GMW Hi-Line 111 is confirmed to have nearly the composition and rheological properties as non-GMW

Use of Opuntia ficus-indica (L.) Mill extracts from Brazilian Caatinga as an alternative of natural moisturizer in cosmetic formulations

ABSTRACT The aim of this work was the obtainment of Opuntia fícus-indica (L.) Mill extract for the development of cosmetic formulations and in vivo evaluation of its moisturizing effects. The formulations were tested for preliminary and accelerated stability. Organoleptic characteristics, pH values and rheological behavior were assessed. The evaluation of moisturizing efficacy of the emulsions formulated with 3.0% of Polyacrylamide (and) C13-14 Isoparaffin (and) Laureth-7 containing 1.0 and 3.0% of O. ficus-indica hydroglycolic extract (EHG001) was performed using the capacitance method (Corneometer(r)) and the transepidermal water loss - TEWL evaluation (Tewameter(r)). The emulsions formulated were stable, exhibiting pseudoplastic and thixotropic behavior. The results of evaluation of moisturizing efficacy showed increased skin hydration after five hours by mainly increasing the skin barrier effect. The formulations containing 1.0 and 3.0% of EHG001 enhanced the skin barrier effect by reducing TEWL up to four hours after application. The results observed suggest that O. ficus-indica hydroglycolic extract may act through a humectant and occlusion mechanism

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. Methods: We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. Findings: Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. Interpretation: As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. Funding: Bill & Melinda Gates Foundation

Bioactive Phytochemicals from Nigella sativa Oil Processing By-products

Nigella sativa (black cumin) is an annual flowering plant grown in the Middle East, Asia, and Mediterranean regions. Black cumin seeds are well known in history with their therapeutic effects in various cultures and also are used for culinary applications. Oil extraction is possible with different methods, but nowadays, the cold press method is gaining importance with the modern customers’ healthy product preferences and its by-products rich in bioactive phytochemicals. The main compounds of seedcakes are carbohydrates, proteins, minerals, fibers, flavonoids, phenolics, and vitamins. Even though the seedcakes are used only as fodder for animals, recent studies have shown the seedcakes’ potential as a value-added raw material or additives for functional food, supplements, cosmeceuticals, and pharmaceuticals with their antioxidant, antimicrobial, and nutritional effects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG

Towards energy-efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Publisher Copyright: © 2023 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand-side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance-Level ElectricityUK-DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.Peer reviewe