Xanthan gum-based edible coating effectively preserve postharvest quality of 'Gola' guava fruits by regulating physiological and biochemical processes.

BACKGROUND: Guava is a fruit prone to rapid spoilage following harvest, attributed to continuous and swift physicochemical transformations, leading to substantial postharvest losses. This study explored the efficacy of xanthan gum (XG) coatings applied at various concentrations (0.25, 0.5, and 0.75%) on guava fruits (Gola cultivar) over a 15-day storage period. RESULTS: The results indicated that XG coatings, particularly at 0.75%, substantially mitigated moisture loss and decay, presenting an optimal concentration. The coated fruits exhibited a modified total soluble soluble solids, an increased total titratable acidity, and an enhanced sugar-acid ratio, collectively enhancing overall quality. Furthermore, the XG coatings demonstrated the remarkable ability to preserve bioactive compounds, such as total phenolics, flavonoids, and antioxidants, while minimizing the levels of oxidative stress markers, such as electrolyte leakage, malondialdehyde, and H2O2. The coatings also influenced cell wall components, maintaining levels of hemicellulose, cellulose, and protopectin while reducing water-soluble pectin. Quantitative analysis of ROS-scavenging enzymes, including superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase, revealed significant increases in their activities in the XG-coated fruits compared to those in the control fruits. Specifically, on day 15, the 0.75% XG coating demonstrated the highest SOD and CAT activities while minimizing the reduction in APX activity. Moreover, XG coatings mitigated the activities of fruit-softening enzymes, including pectin methylesterase, polygalacturonase, and cellulase. CONCLUSIONS: This study concludes that XG coatings play a crucial role in preserving postharvest quality of guava fruits by regulating various physiological and biochemical processes. These findings offer valuable insights into the potential application of XG as a natural coating to extend the shelf life and maintain the quality of guava fruits during storage.

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.

PURPOSE: Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. METHODS: Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. RESULTS: ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. CONCLUSION: The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

The impact of geopolitical risk, institutional governance and green finance on attaining net-zero carbon emission.

This research investigates the impact of geopolitical risk, institutional governance and green finance on environmental outcomes, specifically focusing on carbon emissions and ecological footprint. Utilizing the dynamic CS-ARDL method and aggregated mean group analysis on a panel dataset covering 21 nations from 2000 to 2021, our findings reveal that heightened geopolitical risk leads to both short and long run increases in carbon emissions and the ecological footprint. Our study finds both a direct as well as indirect connection between governance, green finance and environmental outcomes in both the short and long run, highlighting the nuanced impact of governance on the formulation of environmental policies and regulatory frameworks. The results emphasize the need for targeted strategies, including focused investments and incentives for sustainable finance, particularly in conflict-affected regions. Furthermore, our research underscores the enduring impact of historical events, such as wars, on contemporary environmental indicators, emphasizing the importance of proactive conflict prevention measures. Our research suggests that policymakers should adopt comprehensive strategies that prioritize emission reduction during short-run spikes in geopolitical risk while maintaining a steadfast commitment to long-run sustainability.

Anti-inflammatory potential of some eudesmanolide and guaianolide sesquiterpenes.

Ten sesquiterpene lactones isolated from Anvillea garcinii (Burm.f.) DC ethanolic extract were assessed for their anti-inflammatory potential by myeloperoxidase (MPO) activity assignment, and mice paw swelling model. 3α,4α-10ß-trihydroxy-8α-acetyloxyguaian-12,6α-olide (1), epi-vulgarin (3), 9a-hydroxyparthenolide (4), garcinamine C (7), garcinamine D (8), garcinamine E (9), and 4, 9-dihydroxyguaian-10(14)-en-12-olide (10) showed explicit anti-inflammatory activity in rodent paw edema and MPO assignment. The findings of this study showed that the α-methylene γ-lactone moiety does not always guarantee an anti-inflammatory effect, but the presence of proline at the C3 of the lactone ring improves the binding of sesquiterpene lactones with MPO isoenzymes, resulting in a more potent inhibition.

Role of Functional Groups in Tuning Luminescence Signature of Solution-Processed Graphene Quantum Dots: Experimental and Theoretical Insights.

Zero-dimensional graphene quantum dots (GQDs) present unique optoelectronic properties in the large-spectrum range from UV to visible. However, the origin of luminescence in GQDs is still a debatable question. Therefore, the present work investigates the features of trap-mediated and edge-state-functionalized group-associated luminescence enhancement of GQDs. The attached functional groups' involvement in the upsurge of photoluminescence has been discussed theoretically as well as experimentally. In addition, the role of the aromatic ring, the functional group attached, and their positions of attachment to the aromatic ring to tune the emission wavelength and Raman modes have been elucidated theoretically as well as experimentally. We found that in the case of the -OH group attached outside of the aromatic ring, the long-range π hybridization dominates, which suggests that the emission from this model can be dictated by long-range π hybridization. In particular, we found that oxygen-containing functional groups attached outside of the aromatic ring are the main source of the luminescence signature in GQDs. Furthermore, density functional theory (DFT) indicates that the -OH functional group attached outside of the aromatic ring perfectly matched with our experimental results, as the experimental bandgap (2.407 eV) is comparable with the theoretical simulated bandgap (2.399 eV) of the -OH group attached outside of the aromatic ring.

Small RNAs Activate Salmonella Pathogenicity Island 1 by Modulating mRNA Stability through the hilD mRNA 3' Untranslated Region.

Salmonella enterica serovar Typhimurium is an enteric pathogen associated with foodborne disease. Salmonella invades the intestinal epithelium using a type three secretion system encoded on Salmonella pathogenicity island 1 (SPI-1). SPI-1 genes are tightly regulated by a complex feed-forward loop to ensure proper spatial and temporal expression. Most regulatory input is integrated at HilD, through control of hilD mRNA translation or HilD protein activity. The hilD mRNA possesses a 310-nucleotide 3' untranslated region (UTR) that influences HilD and SPI-1 expression, and this regulation is dependent on Hfq and RNase E, cofactors known to mediate small RNA (sRNA) activities. Thus, we hypothesized that the hilD mRNA 3' UTR is a target for sRNAs. Here, we show that two sRNAs, SdsR and Spot 42, regulate SPI-1 by targeting different regions of the hilD mRNA 3' UTR. Regulatory activities of these sRNAs depended on Hfq and RNase E, in agreement with previous roles found for both at the hilD 3' UTR. Salmonella mutants lacking SdsR and Spot 42 had decreased virulence in a mouse model of infection. Collectively, this work suggests that these sRNAs targeting the hilD mRNA 3' UTR increase hilD mRNA levels by interfering with RNase E-dependent mRNA degradation and that this regulatory effect is required for Salmonella invasiveness. Our work provides novel insights into mechanisms of sRNA regulation at bacterial mRNA 3' UTRs and adds to our knowledge of post-transcriptional regulation of the SPI-1 complex feed-forward loop. IMPORTANCE Salmonella enterica serovar Typhimurium is a prominent foodborne pathogen, infecting millions of people a year. To express virulence genes at the correct time and place in the host, Salmonella uses a complex regulatory network that senses environmental conditions. Known for their role in allowing quick responses to stress and virulence conditions, we investigated the role of small RNAs in facilitating precise expression of virulence genes. We found that the 3' untranslated region of the hilD mRNA, encoding a key virulence regulator, is a target for small RNAs and RNase E. The small RNAs stabilize hilD mRNA to allow proper expression of Salmonella virulence genes in the host.

Dual-function C2H2-type zinc-finger transcription factor GmZFP7 contributes to isoflavone accumulation in soybean.

Isoflavones are a class of secondary metabolites produced by legumes and play important roles in human health and plant stress tolerance. The C2H2 zinc-finger transcription factor (TF) functions in plant stress tolerance, but little is known about its function in isoflavone regulation in soybean (Glycine max). Here, we report a C2H2 zinc-finger TF gene, GmZFP7, which regulates isoflavone accumulation in soybean. Overexpressing GmZFP7 increased the isoflavone concentration in both transgenic hairy roots and plants. By contrast, silencing GmZFP7 expression significantly reduced isoflavone levels. Metabolomic and qRT-PCR analysis revealed that GmZFP7 can increase the flux of the phenylpropanoid pathway. Furthermore, dual-luciferase and electrophoretic mobility shift assays showed that GmZFP7 regulates isoflavone accumulation by influencing the expression of Isoflavone synthase 2 (GmIFS2) and Flavanone 3 ß-hydroxylase 1 (GmF3H1). In this study, we demonstrate that GmZFP7 contributes to isoflavone accumulation by regulating the expression of the gateway enzymes (GmIFS2 and GmF3H1) of competing phenylpropanoid pathway branches to direct the metabolic flux into isoflavone. A haplotype analysis indicated that important natural variations were present in GmZFP7 promoters, with P-Hap1 and P-Hap3 being the elite haplotypes. Our findings provide insight into how GmZFP7 regulates the phenylpropanoid pathway and enhances soybean isoflavone content.

Identification of genes for seed isoflavones based on bulk segregant analysis sequencing in soybean natural population.

KEY MESSAGE: We identified four hub genes for isoflavone biosynthesis based on BSA-seq and WGCNA methods and validated that GmIE3-1 positively contribute to isoflavone accumulation in soybean. Soybean isoflavones are secondary metabolites of great interest owing to their beneficial impact on human health. Herein, we profiled the seed isoflavone content by HPLC in 1551 soybean accessions grown in two locations for two years and constructed two extreme pools with high (4065.1 µg g-1) and low (1427.23 µg g-1) isoflavone contents to identify candidate genes involved in isoflavone biosynthesis pathways using bulk segregant analysis sequencing (BSA-seq) approach. The results showed that the average sequencing depths were 50.3× and 65.7× in high and low pools, respectively. A total of 23,626 polymorphic SNPs and 5299 InDels were detected between both pools and 1492 genes with different variations were identified. Based on differential genes in BSA-seq and weighted gene co-expression network analysis (WGCNA), four hub genes, Glyma.06G290400 (designated as GmIE3-1), Glyma.01G239200, Glyma.01G241500, Glyma.13G256100 were identified, encoding E3 ubiquitin-protein ligase, arm repeat protein interacting with ABF2, zinc metallopeptidase EGY3, and dynamin-related protein 3A, respectively. The allelic variation in GmIE3-1 showed a significant influence on isoflavone accumulation. The virus-induced gene silencing (VIGS) and RNAi hairy root transformation of GmIE3-1 revealed partial suppression of this gene could cause a significant decrease (P < 0.0001) of total isoflavone content, suggesting GmIE3-1 is a positive regulator for isoflavones. The present study demonstrated that the BSA-seq approach combined with WGCNA, VIGS and hairy root transformation can efficiently identify isoflavone candidate genes in soybean natural population.

Identification of quantitative trait loci and candidate genes for seed folate content in soybean.

KEY MESSAGE: From 61 QTL mapped, a stable QTL cluster of 992 kb was discovered on chromosome 5 for folate content and a putative candidate gene, Glyma.05G237500, was identified. Folate (vitamin B9) is one of the most essential micronutrients whose deficiencies lead to various health defects in humans. Herein, we mapped the quantitative trait loci (QTL) underlying seed folate content in soybean using recombinant inbred lines developed from cultivars, ZH35 and ZH13, across four environments. We identified 61 QTL on 12 chromosomes through composite interval mapping, with phenotypic variance values ranging from 1.68 to 24.68%. A major-effect QTL cluster (qFo-05) was found on chromosome 5, spanning 992 kb and containing 134 genes. Through gene annotation and single-locus haplotyping analysis of qFo-05 in a natural soybean population, we identified seven candidate genes significantly associated with 5MTHF and total folate content in multiple environments. RNA-seq analysis showed a unique expression pattern of a hemerythrin RING zinc finger gene, Glyma.05G237500, between both parental cultivars during seed development, which suggest the gene might regulate folate content in soybean. This is the first study to investigate QTL underlying folate content in soybean and provides new insight for molecular breeding to improve folate content in soybean.

Cottonseed oil: A review of extraction techniques, physicochemical, functional, and nutritional properties.

Seed oils are the richest source of vitamin-E-active compounds, which contribute significantly to antioxidant activities. Cottonseed oil (CS-O) is attaining more consideration owing to its high fiber content and stability against auto-oxidation. CS-O has gained a good reputation in the global edible oil market due to its distinctive fatty acid profile, anti-inflammatory, and cardio-protective properties. CS-O can be extracted from cottonseed (CS) by microwave-assisted extraction (MAE), aqueous/solvent extraction (A/SE), aqueous ethanol extraction (A-EE), subcritical water extraction, supercritical carbon dioxide extraction (SC-CO2), and enzyme-assisted extraction (E-AE). In this review, the importance, byproducts, physicochemical characteristics, and nutritional profile of CS-O have been explained in detail. This paper also provides a summary of scientific studies existing on functional and phytochemical characteristics of CS-O. Its consumption and health benefits are also deliberated to discover its profitability and applications. CS-O contains 26-35% saturated, 42-52% polyunsaturated, and 18-24% monounsaturated FA. There is approximately 1000 ppm of tocopherols in unprocessed CS-O, but up to one-third is lost during processing. Moreover, besides being consumed as cooking oil, CS-O discovers applications in many fields such as biofuel, livestock, cosmetics, agriculture, and chemicals. This paper provides a comprehensive review of CS-O, its positive benefits, fatty acid profile, extraction techniques, and health applications.HighlightsCS-O is a rich source of exceptional fatty acids.Various techniques to extract the CS-O are discussed.Numerous physicochemical properties of CS-O for the potential market are assessed.It has a wide range of functional properties.Nutritional quality and health benefits are also evaluated.

Combination of metformin and sub-therapeutic dose of valproic acid prevent valproic acid-induced toxicity in animal model of epilepsy.

Valproic acid (VPA) is one of the most prescribed drugs for epilepsy. Extended use of VPA not only induces hepatotoxicity but also impairs the cognitive functions. Metformin has been reported to prevent epileptogenesis and enhance memory. To counter the VPA-induced adverse events, it is hypothesized that combination of sub-therapeutic dose of VPA with metformin may attenuate the toxicity stemming from the therapeutic dose of VPA. Pentylenetetrazole (PTZ)-induced kindling model of epilepsy in mice was used to assess the combined effects of sub-therapeutic dose of VPA (100 mg/kg) and metformin (200 mg/kg). The memory performance was analyzed by passive avoidance test, while alkaline comet assay was used to determine genotoxicity. Histopathological examination and serum biochemical analysis was performed to determine hepatotoxicity. Results showed that combination dose of VPA with metformin reduced seizure scores. VPA (300 mg/kg) administered as a single agent did not enhance memory impairment caused by PTZ, however, combination of sub-therapeutic dose of VPA with metformin enhanced memory function. Furthermore, in alkaline comet assay, combination therapy demonstrated reduced genotoxicity compared to the VPA 300 mg/kg. Histopathological examination of liver and analysis of serum hepatic enzymes revealed that combination therapy (VPA + metformin) reversed the toxicity as seen in case of PTZ or VPA (300 mg/kg) treated animals with no other treatment given. Based on the study data, it is concluded that the combination of sub-therapeutic dose of VPA with metformin might be used for epileptic seizures. This will prevent the hepatotoxicity and enhanced memory functions as compared to the VPA given as a single agent therapy.

A systematic review and meta-analysis of the use of methylene blue to improve the lymph node harvest in rectal cancer surgery.

BACKGROUND: Methylene blue staining of the resected specimen has been described as an alternative to the conventional palpation and visual inspection method to improve lymph node harvest. This meta-analysis evaluates the usefulness of this technique in surgery for rectal cancer, particularly after neoadjuvant therapy. METHODS: Randomized controlled trials (RCTs) comparing lymph node harvest in methylene blue-stained rectal specimens to those of unstained specimens were identified from the Medline, Embase, and Cochrane databases. Non-randomized studies and those with only colonic resections were excluded. The quality of RCTs was assessed using Cochrane's risk of bias tool. A weighted mean difference (WMD) was calculated for overall harvest, harvest after neoadjuvant therapy, and metastatic nodal yield. In contrast, the risk difference (RD) was calculated to compare yields of less than 12 lymph nodes between the stained and unstained specimens. RESULTS: Study selection comprised seven RCTs with 343 patients in the unstained group and 337 in the stained group. Overall lymph node harvest and harvest after neoadjuvant therapy were significantly higher in stained specimens with a WMD of 13.4 and 10.6 and a 95% confidence interval (CI) of 9.5-17.2 and 4.8-16.3, respectively. Harvest of metastatic lymph nodes was significantly higher in the stained group (WMD 1.0, 95% CI 0.6-1.4). The yield of less than 12 lymph nodes was significantly higher in the unstained group with RD of 0.292 and 95% CI of 0.182-0.403. CONCLUSION: Despite a small number of patients, this meta-analysis confirms improved lymph node harvest in surgical specimens stained with methylene blue compared with unstained specimens.

Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency.

Smart meter datasets have recently transitioned from monthly intervals to one-second granularity, yielding invaluable insights for diverse metering functions. Clustering analysis, a fundamental data mining technique, is extensively applied to discern unique energy consumption patterns. However, the advent of high-resolution smart meter data brings forth formidable challenges, including non-Gaussian data distributions, unknown cluster counts, and varying feature importance within high-dimensional spaces. This article introduces an innovative learning framework integrating the expectation-maximization algorithm with the minimum message length criterion. This unified approach enables concurrent feature and model selection, finely tuned for the proposed bounded asymmetric generalized Gaussian mixture model with feature saliency. Our experiments aim to replicate an efficient smart meter data analysis scenario by incorporating three distinct feature extraction methods. We rigorously validate the clustering efficacy of our proposed algorithm against several state-of-the-art approaches, employing diverse performance metrics across synthetic and real smart meter datasets. The clusters that we identify effectively highlight variations in residential energy consumption, furnishing utility companies with actionable insights for targeted demand reduction efforts. Moreover, we demonstrate our method's robustness and real-world applicability by harnessing Concordia's High-Performance Computing infrastructure. This facilitates efficient energy pattern characterization, particularly within smart meter environments involving edge cloud computing. Finally, we emphasize that our proposed mixture model outperforms three other models in this paper's comparative study. We achieve superior performance compared to the non-bounded variant of the proposed mixture model by an average percentage improvement of 7.828%.

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors.

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.

Levetiracetam ameliorates epileptogenesis by modulating the adenosinergic pathway in a kindling model of epilepsy in mice.

Background: Levetiracetam (LEV) has been found to have an antihyperalgesic effect via acting on the adenosine system. However, the effects of LEV on the modulation of the adenosine system in the brain have not been elucidated in the prevention of seizures and epilepsy. The present study aimed to explore the possible LEV mechanisms of action in the adenosine signaling systems in an animal model of epilepsy. Methodology: A docking study was initially performed to determine the possible interaction of LEV with adenosine A1 receptors (A1Rs) and equilibrative nucleoside transporters-1 (ENT1). The experimental study was divided into an acute seizure test (32 mice distributed into 4 groups) and a chronic kindling model study (40 mice distributed into 5 groups), followed by gene expression analysis and immunohistochemistry. The kindling model lasted 26 days and took 13 subconvulsive doses of pentylenetetrazole (PTZ) to completely kindle the mice in the PTZ control group. Gene expression changes in the A1Rs, potassium inwardly-rectifying channel 3.2 (Kir3.2), and ENT1 in the brain tissue samples of the mice following treatment with LEV were analyzed using reverse transcription-quantitative polymerase chain reaction, and immunohistochemistry was performed for the A1R protein expression. Results: Docking studies predicted a significant interaction of LEV with A1Rs and ENT1 proteins. Results from the acute testing revealed that caffeine (100 mg/kg) and 8-cyclopentyl-1,3-dipropylxanthine (25 mg/kg) significantly reversed the antiseizure effects of LEV by reversing the percent protection and shortening the onset of the first myoclonic jerk (FMJ) and generalized clonic seizures (GCSs). In the PTZ-induced kindling, LEV demonstrated an increased gene expression of A1Rs and Kir3.2 in the brain. LEV also significantly reduced the gene expression of ENT1. Furthermore, the immunohistochemical analysis showed that LEV increased the protein expression of A1Rs in the brain. Conclusion: Based on these results, it can be concluded that LEV modulates epileptogenesis by acting on the adenosine pathway in the central nervous system.

Modeling and forecasting the total number of cases and deaths due to pandemic.

The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R2 ) and lower root-mean-square error and the mean absolute percentage error (MAPE). The values of R2 are greater than 99% for all countries other than China whereas for China this R2 was 97%. The high values of R2 and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.

Robust p53 recovery using chattering free sliding mode control and a gain-scheduled modified Utkin observer.

p53 protein plays an essential role in protecting the genomic integrity of mammalian cells. A drastic decrease in the amount of p53 protein has been observed in cancerous cells. By using Nutlin-based small molecule drugs, the concentration of p53 can be restored to the desired level. This paper presents the drug-dosage design for p53 pathway, based on a control-oriented nonlinear model. A chattering free sliding mode control (CFSMC) strategy is employed to track the desired trajectory of p53 concentration for both of its dynamic behaviors, i.e., sustained and oscillatory responses. A gain-scheduled modified Utkin observer (GSMUO) is designed for robust state reconstruction and disturbance estimation. The simulation results show that CFSMC and GSMUO exhibit desired robustness and performance properties in the presence of parametric variations, an input disturbance and measurement noise. Moreover, a comprehensive simulation study, along with a detailed quantitative analysis is performed to compare CFSMC-GSMUO with four different techniques: a sliding mode control (SMC) with an equivalent control based sliding mode observer (SMO) and GSMUO, respectively, and a dynamic sliding mode control (DSMC) with SMO and GSMUO, respectively. The analysis demonstrates that the tracking error and utilization of the control energy is the least in the case of CFSMC-GSMUO as compared to its counterparts.

ATP-binding cassette transporters expression profiling revealed its role in the development and regulating stress response in Solanum tuberosum.

The ATP-binding cassette (ABC) transporter gene family plays a vital role in substance transportation, including secondary metabolites, and phytohormones across membranous structures. It is still uncovered in potato (Solanum tuberosum), grown worldwide as a 3rd important food crop. The current study identified a total of 54 Stabc genes in potato genome. The accumulative phylogenetic tree of Stabc with arabidopsis, divided into eight groups (ABCA to ABCH). ABCG was the most prominent group covering 90% of Stabc genes, followed by ABCB group. The number and architecture of exon-intron varied from gene to gene. In addition, the presence of stress-responsive elements in the regulatory regions depicted their role in environmental stress. Furthermore, the tissue-specific and stress-specific expression profiling of Stabc genes and their validation through real-time-qPCR analysis revealed their role in development and stress. The presented results provided useful information for further functional analysis of Stabc genes and can also use as a reference study for other important crops.

How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science.

To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.

Process Simulation and Life Cycle Assessment of Waste Plastics: A Comparison of Pyrolysis and Hydrocracking.

Pyrolysis and hydrocracking of plastic waste can produce valuable products with manageable effects on the environment as compared to landfilling and incineration. This research focused on the process simulation and life cycle assessment of the pyrolysis and hydrocracking of high-density polyethylene. Aspen Plus was used as the simulator and the Peng-Robinson thermodynamic model was employed as a fluid package. Additionally, sensitivity analysis was conducted in order to optimize product distribution. Based on the simulation, the hydrocracking process produced value-added fuels, i.e., gasoline and natural gas. In contrast, pyrolysis generated a significant quantity of pyrolysis oil with a high number of cyclo-compounds and char, which are the least important to be utilized as fuels. Moreover, in the later part of the study, life cycle assessment (LCA) was adopted in order to investigate and quantify their impact upon the environment using simulation inventory data, which facilitates finding a sustainable process. Simapro was used as a tool for LCA of the processes and materials used. The results demonstrate that hydrocracking is a better process in terms of environmental impact in 10 out of the 11 impact categories. Overall, the present study proposed a promising comparison based on energy demands, product distribution, and potential environmental impacts, which will help to improve plastic waste management.