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Abstract Background Evidence for the efficacy of pharmacological therapies for heart failure with reduced ejection fraction (HFrEF) is growing. However, there is no consensus on the most effective treatment for HFrEF. This study aimed to evaluate the most effective combination of pharmacological therapy in patients with HFrEF. Methods We systematically searched Medline, Embase, and CENTRAL up to Feb 2022, to include randomized controlled trials (RCTs) that evaluated the efficacy of pharmacological treatment among adults (≥ 18 years) with a diagnosis of HFrEF (defined by a left ventricular ejection fraction ≤ 45%). The outcomes of interest included all-cause death, cardiovascular (CV) death, and hospitalization for heart failure (HHF). A random network meta-analysis using a frequentist framework model was employed to calculate the pooled risk ratio (RR) with 95% confidence interval (CI) and rank the treatments. Results We included 49 RCTs involving 90,529 participants with HFrEF. For reducing all-cause mortality, the combination of angiotensin-converting enzyme inhibitors (ACEI), beta-blockers (BB), mineralocorticoid receptor antagonists (MRA), and sodium-glucose co-transporter-2 inhibitors (SGLT2i) was most effective (RR, 0.46; 95% CI, 0.32–0.66). For CV death, the combination of ACEI, BB, MRA, and Vericiguat showed the highest efficacy (RR, 0.34; 95% CI, 0.12–0.90). Regarding reducing HHF, the combination of ACEI, BB, MRA, and SGLT2i as well as the combination of ACEI, BB, MRA, and Ivabradine were equally the most effective (both RR, 0.27; 95% CI, 0.18–0.39). Conclusion This study provides robust evidence supporting the use of combination therapies in HFrEF management, with newer agents offering incremental benefits when added to established guideline-directed medical therapy.

Based on joint progressive Type-II censored data, we examined the statistical inference of the generalized logistic distribution with different shape and scale parameters in this research. Wherever possible, we explored maximum likelihood estimators for unknown parameters within the scope of the joint progressive censoring scheme. Bayesian inferences for these parameters were demonstrated using a Gamma prior under the squared error loss function and the linear exponential loss function. It was important to note that obtaining Bayes estimators and the corresponding credible intervals was not straightforward; thus, we recommended using the Markov Chain Monte Carlo method to compute them. We performed real-world data analysis for demonstrative purposes and ran Monte Carlo simulations to compare the performance of all the suggested approaches.

Abstract Since the beginning of humanity, people have used wild medicinal plants for the treatment of various diseases. Nowadays, researchers and scientists pay attention to exploring new tools to maximize the efficacy of natural products from natural resources, and among these tools formulation of nanoparticles is very promising. The green synthesis of metal/metal oxide nano-solutions using the Deverra tortuosa extract has still not been explored well. This study aims to prepare many metal/metal oxide nanoparticle solutions such as ZnO-NPs, SeO2-MNPs, and Ag-NPs via the methanol extract of D. tortuosa as safe, easy, green, and economic approach as well as characterize the chemical components of D. tortuosa plant by GC-MS spectroscopy analysis. The synthesized M-NPs solutions were characterized by UV–visible (UV–Vis) spectroscopy, Transmission Electron Microscope (TEM), zeta potential, Fourier Transform Infrared (FT-IR) spectroscopy, and phytochemical analyses. The potential antioxidant capacity was estimated for the methanol extract of this plant along with the metal/metal oxide nanoparticles solutions by DPPH assay. In addition, the cytotoxic activity was in vitro assessed for the inspected samples against various tumor and normal cell lines applying MTT assay. The application of the D. tortuosa methanolic extract resulted in NPs with a range of 11.79–85.9 nm which was characterized by UV-Vis spectroscopy, TEM, zeta potential, and FT-IR spectroscopy that revealed various functional groups of the tested samples. The GC-MS analysis of the D. tortuosa methanolic extract showed the presence of 31 chemical compounds with 2-methyl-3-oxocyclopent-1-ene-1-carboxylic acid, methyl oleate, and 6-allyl-4,5-dimethoxybenzo[d][1,3]-dioxole as major constituents. The extract showed considerable antioxidant activity as well as antimicrobial activity that was assessed also against varied bacterial and fungal species. Remarkable potencies for the investigated metal/metal oxide nanoparticle solutions hinder the growth of the tumor cell lines, in addition to the growth of the microbial species. Based on the determined biological activities of the produced NPs, future study is recommended to characterize the pure authentic compounds that are identified within the D. tortuosa as major compounds as well as evaluate their modes of action.

Abstract Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

Abstract Breast cancer is among the highest morbidity and mortality rates in women around the world. In the present investigation we aimed to synthesis novel nanosystem combining two naturally important anticancer agents with different mechanism of action namely Moringa oleifera and caffeine. Firstly, chemical analysis of Moringa oleifera extract and caffeine was done by gas chromatography-mass spectroscopy (GC–MS) in order to assess the main chemical compounds present and correlate between them and the possible anticancer effect. The novel nanosystem was characterized through dynamic light scattering techniques which revealed the stability and homogeneity of the prepared M. oleifera leaves extract/Caffeine loaded chitosan nanoparticles, while FTIR and transmission electron microscope (TEM) proved the shape and the successful incorporation of M. oleifera leaves extract/Caffeine onto the nanochitosan carrier. Our initial step was to assess the anticancer effect in vitro in cancer cell line MCF-7 which proved the significant enhanced effect of M. oleifera leaves extract/Caffeine nanosystem compared to M. oleifera leaves extract or caffeine loaded nanoparticles. Further studies were conducted in vivo namely tumor biomarkers, tumor volume, bioluminescence imaging, molecular and histopathological investigations. The present study proved the potent anticancer effect of the synthesized M. oleifera leaves extract/Caffeine loaded chitosan nanoparticles. Mo/Caf/CsNPs exhibited a large number of apoptotic cells within the tumor mass while the adipose tissue regeneration was higher compared to the positive control. The prepared nanoparticles downregulated the expression of Her2, BRCA1 and BRCA2 while mTOR expression was upregulated. The aforementioned data demonstrated the successful synergistic impact of Moringa and caffeine in decreasing the carcinoma grade.

Abstract This study investigated the prevalence, morphology, molecular identification, and histopathological effects of larval tapeworms (plerocercoids) infecting the skeletal muscles of the Indian halibut (Psettodes erumei) collected from the coastal waters of the Arabian Gulf. Numerous oval or round blastocysts, measuring 13–26 mm, were found embedded within the muscular tissues of the Indian halibut, rendering the fish unsuitable for human consumption. Morphological and molecular analyses identified the plerocercoids as Dasyrhynchus giganteus (family Dasyrhynchidae), with an overall prevalence of 15.4%. The seasonal prevalence was the highest in summer (14.6%), followed by spring (10.6%), winter (4.4%), and autumn (3.5%). Infection rates increased with fish size. Histopathological examination revealed fibrous connective tissue capsules surrounding the larvae, causing muscular atrophy and degenerative changes, with few inflammatory eosinophilic cells. Molecular and phylogenetic analysis of the 28S rDNA gene sequences confirmed the specimens as D. giganteus, clustered closely with other sequences of D. giganteus with 100% bootstrap values. This study provided valuable insights into the parasitic infection dynamics, seasonal variation, molecular identification, and histopathological effects, highlighting the importance of monitoring fish for food safety and public health implications.

Aims: Traditionally, crustacean wastes have been managed by using acid and alkali which leads to major environmental issue. However, over the recent years microbial fermentation has gained its way whereby producing similar effects as chemical treatment and a higher quality product can be obtained. Extracellular protease from Bacillus subtilis was used further by optimizing its culture medium to enhance protease production. Methodology and Results: The culture media was optimized with 4 various sources; Shrimp Crab Shell Powder (SCSP), nitrogen sources, inorganic salts, and carbon sources. It was found that culture media supplemented with 9% SCSP, 3% yeast extract, 1% sodium chloride and 9% glucose augmented protease activity up to 565.80 ± 19.41 U/mL compared to the un-optimized media (170.57 ± 6.75 U/mL). By using this optimized media, the ability and efficiency of B. subtilis in a period of 6 days was investigated whereby acid treated shrimp shells (ATSS) and raw shrimp shell powder (RSSP) were used in substitution of SCSP. In a period of 6 days, the protein content in both ATSS and RSSP was found to have been removed up to 60% and 42% respectively. However deproteinization was found to be more efficient in RSSP with the ratio of tyrosine to protein remained constantly high throughout the 6 days period. Conclusion, significance and impact of study: A better, more efficient and environmental friendly method iscontinuously being improvised to manage shrimp wastes with the use of microbes.

Geothermal brines contain high concentrations of ions and form a source of various valuable elements. The isolation of the elements from their water systems is a great challenge when the gradual depletion of ores in mining is considered. Attempts have been made for a long time to isolate valuable elements from aqueous mixtures prepared in the laboratory. However, those studies might not reflect the complexity of natural systems and might yield results that deviate significantly from the performance in real field systems. In this study, sorption is used to extract lithium ions from a representative field, Tuzla Geothermal Power Plant (TGPP) Turkey, using a mini-pilot reactor introduced to the reinjection well of the plant. Electrolytic manganese dioxide (γ-MnO2), a relatively inexpensive material widely used as the cathode material in lithium-ion batteries, was employed as a sorbent material for lithium. The sorption/desorption performance of the novel γ-MnO2 was investigated under various conditions. Sorption is performed at 360K and 2 bars. The maximum sorption performance was obtained at 1 h in Tuzla GPP. The desorption experiments were performed in acidic solutions. The concentration of Li+ in the desorption solution was found to be 25 mg/L on average when 10 g of γ-MnO2 was dispersed into 30 mL of the acidic aqueous solution. The first desorption solution was used consecutively for collecting more Li+ ions through the desorption of fresh brine-treated powder samples (cumulative desorption). By repeating this process four times consecutively, 230 mg/L of Li+ was obtained in the desorption solution. Moreover, the reusability of the γ-MnO2 sorbent was examined. The sorbent powder showed almost 40% performance efficiency compared to virgin powder under the conditions employed in this study. The use of electrolytic γ-MnO2 sorbent for lithium adsorption was found to be a promising process for practical use in the separation of lithium from geothermal brines.

Abstract One of the major problems of using nanofluids in heat exchange applications is the forming and deposition of nanoparticles on the inner surface of the heat exchanger. In this paper, Water-Cmc fluid is used as a surfactant for nanoparticles to prevent deposition and congregation. The pressure drops and heat transfer in U-bend double pipe heat exchanger based on water-MgO-Cmc fluid, are examined. Nanoparticles of Magnesium Oxide (MgO) and Carboxymethyl Cellulose (Cmc) are used with pure water as a base fluid. The experimental rig and procedures are designed to facilitate various operational conditions such as flow rate, volume concentration of MgO particles and weight concentration of Cmc particles. Furthermore, convective heat transfer coefficient, heat exchanger effectiveness, pressure drop, friction factor, under different conditions, are measured. The results demonstrate convective heat transfer coefficient of U-bend double pipe heat exchangers is enhanced by 35% for 1 MgO vol.% and 0.2 Cmc wt.% compared to base fluid (Water-Cmc). It is concluded that pressure drops are directly proportion to the increase of MgO nanoparticles at same Cmc concentration by 23% at 0.2 wt.%. Whilst, friction factor of the system is inversely proportion to the increase of volumetric flow rate of water-MgO-Cmc fluid. An increase in MgO nanoparticle concentration increases the friction factor, hence maximum friction factor enhancement by 38% for MgO concentration of 1 vol.%. The effectiveness of heat exchanger is slightly increased by 8% with increase of MgO concentration and flow rate. Finally, thermo-physical characteristics of water-MgO-Cmc fluid at various temperatures, are measured

Aims: The study aimed to assess the midfacial soft and hard tissue changes of males and females following maxillary skeletal expansion in young adults. Materials and methods: 24 patients, 11 males, and 13 females (mean age, 19.54 years; between 17 to 27) with maxillary transverse deficiency treated with MSE. CBCT scans taken before and after expansion were used to measure the changes in soft and hard tissue landmarks. Paired t-tests were used for statistical analyses. Results: Hard tissue lower interzygomatic distance increased by (3.41mm in males, and 2.97mm in females), intermolar distance increased by (5.84mm in males, and 5.51mm in females), the palatal separation anteriorly was (4.14mm in males, and 3.76mm in females), posteriorly was (3.24 mm in males, and 3.25 mm in females), however nasal width increased by (3.49mm in males, and 3.29mm in females). Soft tissue average lateral movement of the alar curvature points was (2.1mm in males, and 1.98mm in females). the nasolabial fold width was (3.54mm in males, and 2.55mm in females). and interzygomatic width was (2.27mm in males, and 1.85mm in females). Conclusions: Maxillary expansion using MSE resulted in an almost parallel expansion with significant lateral movements of the soft tissues of the cheek and alar curvature points on both sides in young adults and it was correlated with the hard tissue changes.

An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.

Salinity is a significant factor restricting plant growth and production. The effect of salinity stress on different growth parameters of 111 fenugreek genotypes was examined in an experiment with three salinity levels (0, 3000, 6000 mgL−1). A completely randomized block design with two replicated pots per treatment was used. Non-significant treatment effects were observed on fresh weight (FW); however, all traits showed significant genotype-by-treatment (GxT) interactions. This GxT was reflected in substantial SNP x environment interactions. Of 492 significant SNPs associated with the measured traits, 212 SNPs were linked to the correlated traits using an arbitrary threshold of three. Several SNPs were associated with FW and dry weight, measured under the same salinity treatment. The correlation between both traits was 0.98 under the three salinity treatments. In addition, 280 SNPs with conditional neutrality effects were mapped. The identified SNPs can be used in future marker-assisted breeding programs to select salt-tolerant genotypes. The results of this research shed light on the salt-tolerant properties of fenugreek.

The objective of this research was to explore the link between Helicobacter pylori (H. pylori) infection and alterations in ischemia modified albumin (IMA), thiol, and disulfide levels, with a focus on their potential clinical implications.

Spinal cord injury (SCI) results in severe atrophy of skeletal muscle in paralyzed regions, and a decrease in the force generated by muscle per unit of cross-sectional area. Oxidation of skeletal muscle ryanodine 1 receptors (RyR1) reduces contractile force as a result of reduced binding of calstabin 1 to RyR1. One cause of RyR1 oxidation is nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (Nox4). We have previously shown that, in rats, RyR1 was oxidized and bound less to calstabin 1 at 56 days after SCI by spinal cord transection. Here, we used a conditional knockout (KO) mouse model of Nox4 in skeletal muscle to investigate the role of Nox4 in reduced muscle specific force after SCI. Peak twitch force of extensor digitorum longus muscles in control mice after SCI was reduced by 42% compared with sham-operated controls, but was increased by ?43% in SCI Nox4 conditional KO mice compared with SCI controls, although it remained less than that for sham-operated controls. Unlike what was previously observed in rats after SCI, the expression of Nox4 was not increased in gastrocnemius muscle, and binding of calstabin 1 to RyR1 was not reduced in this muscle. The results suggest that Nox4 is directly involved in reduction in muscle twitch force after SCI, although further studies are needed to understand the mechanistic basis for this linkage.

Cardiac allografts suffer diastolic dysfunction early post-heart transplantation (HTx) due to ischemic injury, however the natural course of diastology recovery post HTx remains unknown (Tallaj et al., 2007 [1]). We retrospectively reviewed 60 adult HTx patients between 2015 and 2021 at a single site. Invasive hemodynamics and echocardiograms were obtained at 2 weeks and 1, 3, 6, and 12 months post-HTx. RA strain by 2D feature tracking was compared to intracardiac pressure measurements. In all patients, we observed normalization of RV and RA filling pressures by post-operative week 12 and recovery of diastolic dysfunction by month 6. There was an inverse correlation between RV end-diastolic pressure and RA contractile (r = −0.192, p

Background: Left ventricular assist devices (LVADs) may induce electromagnetic interference (EMI) affecting implanted cardiac devices, including more novel subcutaneous implantable cardiac defibrillators (S-ICDs). Methods: In this case series, the authors retrospectively reviewed courses of 6 patients with S-ICDs who underwent LVAD implantation at a single center. Results: Of the 6 patients reviewed, 4 experienced inappropriate ICD shocks, of which 3 resulted from EMI. Five of the 6 patients ultimately had S-ICD therapies disabled. Conclusions: Due to EMI resulting in inappropriate shocks and improved tolerability of malignant arrhythmias, deactivation or removal of S-ICDs should be considered in patients undergoing LVAD implantation.

Introduction: Diuretics are the mainstay of maintaining and restoring euvolemia in the management of heart failure. Loop diuretics are often preferred, however, combination diuretic therapy (CDT) with a thiazide diuretic is often used to overcome diuretic resistance and increase diuretic effect. We performed an analysis of the GUIDE-IT study to assess all-cause mortality and time to first hospitalizations in patients necessitating CDT. Methods: Patients from the GUIDE-IT dataset were stratified by their requirement for CDT with a thiazide to achieve euvolemia. A total of 894 patients were analyzed, 733 of which were treated with loop diuretics alone vs 161 used either chlorothiazide or metolazone in addition to loop diuretics. Kaplan-Meir curves were derived with log-rank p-values to evaluate for differences between the groups. Results: There was no significant difference in all-cause mortality regardless of CDT utilization status (mean survival of 612.704 days vs 603.326 days, p = 0.083). On subgroup analysis, there was no significant difference in all-cause mortality amongst those using loop diuretics compared to CDT in the BNP-guided therapy group, (mean survival time 576.385 days vs 620.585 days, p = 0.0523), nor the control group (614.1 days vs 588.9 days; p = 0.5728). Time to first hospitalization was reduced in all using CDT compared to loop diuretics alone (280.5 days vs 407.2 days, p

The use of recycled concrete aggregate (RAC) in creating new concrete has gained significant attention for environmental and financial reasons. However, the compressive strength of the product concrete is hard to predict due to many variables. In this study, the compressive strength of recycled aggregate concrete was predicted using eight well-known machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), XGboost, Tree, Random Forest, Gradient Boosting, CatBoost, and AdaBoost. Every machine learning algorithm's general methodology entails gathering and analyzing input data, training the algorithm, testing the algorithm, and producing an output. A total of 419 data samples (experimental tests) were used in training and testing all the machine learning models. The results show that the best models for estimating RAC compressive strength are Neural Network, AdaBoost, and XGBoost. The other algorithms, random forest, gradient boosting, and Catboost, performed well in predicting the compressive strength of RAC, however, tree decision and SVM performed badly. The primary evaluation metrics used in this study were Mean Squared Error (MSE) and R-squared (R²), which helped determine the accuracy and reliability of the predictive models.

In the current study, the incline edge crack growth behavior in an aluminum alloy 7075-T6 specimens (which were coated using nickel-carbon nanotubes electroless coating) has been investigated undergoes uniaxial tensile fatigue loading. High purity multi walled carbon nanotubes have been used in nano-coating solutions with various concentrations (1 g/l, 2 g/l and 3 g/l) to examine the improvement extent of fatigue crack growth resistance and other mechanical properties. Water jet technique was used to cutting aluminum plates to the required dimensions before the nano-coating procedure. Multi-purpose fatigue apparatus has been adopted to apply tensile fatigue loading with zero stress ratios. High magnification camera with image j software has been utilized to measure the propagated crack lengths with the number of fatigue cycles. To inspection the quality, thickness uniformity and homogeneity of the nano-coating layer over specimen surfaces, EDXRF and FESEM techniques have been adopted in this study. Numerical simulation using ABAQUS 2021 programming was performed for the purpose of the results validation. The measured results offered a 25.9 %, 44.5 % and 47.9 % increasing in the hardness of the coated specimens using 1 g/l, 2 g/l and 3 g/l MWCNTs respectively. The modulus of elasticity produced an increasing of 26.7 %, 44.4 % and 46.3 % for the coated specimens using 1 g/l, 2 g/l and 3 g/l MWCNTs respectively. The nano-coating with 2 g/l MWCNTs produced more homogeneous, uniform thickness coating layer and fatigue crack growth resistance than that of the coated specimens using other concentrations of MWCNTs. The similarity in the crack growth behavior was the major outcome of the results validation from the fatigue tests and numerical simulation.

A wireless sensor network (WSN) consists of several sensor nodes; all these nodes can sense physical events, including light, heat, and pressure. These networks are essential in smart homes, smart agriculture, and smart water management, swelling with the concept of the Internet of Things. However, WSN needs to address the challenges of energy issues; thus, energy-conserving techniques have been pursued for communication. Optimization of energy is normally solved using the Particle Swarm Optimization (PSO) algorithm since it offers high accuracy but is prone to local optima, thus resulting in early convergence. To tackle this challenge, this paper proposes the development of an enhanced particle swarm optimization for the node power estimation (EPSO-NPE) model. EPSO-NPE calculates distinct transmission powers for each node, preventing the formation of isolated areas within a sensor cluster. Unlike the original PSO, the EPSO algorithm enhances exploration capabilities by avoiding stagnation on search space boundaries. A comparative analysis with the original PSO-based model (PSO-NPE), where nodes adopt maximum power for connectivity, reveals superior performance by EPSO-NPE. The enhanced model exhibits heightened energy-saving capabilities, ultimately extending the network’s lifetime.

In order to evaluate the competitive advantages and dependability of two products in a competitive environment, comparative lifespan testing becomes essential. We examine the inference problems that occur when two product lines follow the Nadarajah-Haghighighi distribution in the setting of joint type-II censoring. In the present study, we derived the maximum likelihood estimates for the Nadarajah-Haghighi population parameters. Additionally, a Fisher information matrix was constructed based on these maximum likelihood estimations. Furthermore, Bayesian estimators and their corresponding posterior risks were calculated, considering both gamma and non-informative priors under symmetric and asymmetric loss functions. To assess the performance of the overall parameter estimators, we conducted a Monte Carlo simulation using numerical methods. Lastly, a real data analysis was carried out to validate the accuracy of the models and methods discussed.

Antifouling membranes were developed by encapsulating amino-functionalized nano-silica in polymeric mixes of polyethersulfone (PES) and polyvinyl pyrrolidone (PVP). Subsequently, the membranes were modified with a nano-Ag/PVP coating layer. The membranes’ performance was assessed against synthetic whey solutions model to mimic dairy wastewater. Various techniques were harnessed to evaluate the membranes' composition, structure and morphology, including SEM, XPS, XRD, AFM, mechanical characteristics, porosities and contact angles. Findings indicated that nanocomposite made with 1.5 % amino-functionalized nano-silica (M3) revealed optimum performance and was capable to separate 90 % of the whey in the feed along with a permeate flux of 238 L/m2.h, when the feed whey concentration was 0.4°Brix. the equivalent membrane but coated with a nano-Ag /PVP layer (M3S) displayed a trivial drop in permeate flux (229.3 L/m2.h) comparing to uncoated one. Nonetheless, the addition of the coating boosted the separation efficiency up to 99.7 % of the whey in the feed. The fouling test indicated that the coated membrane (M3S) had superior antifouling property, where flux recovery ratio (FRR) hit 99.5 % with a diminution in the backwash rate.

Abstract The periodic circumferential cytoskeleton supports various tubular tissues. Radial expansion of the tube lumen causes anisotropic tensile stress, which can be exploited as a geometric cue. However, the molecular machinery linking anisotropy to robust circumferential patterning is poorly understood. Here, we aim to reveal the emergent process of circumferential actin cable formation in a Drosophila tracheal tube. During luminal expansion, sporadic actin nanoclusters emerge and exhibit circumferentially biased motion and fusion. RNAi screening reveals the formin family protein, DAAM, as an essential component responding to tissue anisotropy, and non-muscle myosin II as a component required for nanocluster fusion. An agent-based model simulation suggests that crosslinkers play a crucial role in nanocluster formation and cluster-to-cable transition occurs in response to mechanical anisotropy. Altogether, we propose that an actin nanocluster is an organizational unit that responds to stress in the cortical membrane and builds a higher-order cable structure.

In this research, we present a new distribution, which is the bivariate alpha power Burr-XII distribution, based on the alpha power Burr-XII distribution. We thoroughly examine the key features of our newly developed bivariate model. We introduce a new class of bivariate models, which are built with the copula function. The statistical properties of the proposed distribution, such as conditional distributions, conditional expectations, marginal distributions, moment-generating functions, and product moments were studied. This was accomplished with two datasets of real data that came from two distinct devices. We employed Bayesian, maximum likelihood estimation, and least squares estimation strategies to obtain estimated points and intervals. Additionally, we generated bootstrap confidence intervals and conducted numerical analyses using the Markov chain Monte Carlo method. Lastly, we compared this novel bivariate distribution’s performance to earlier bivariate models, to determine how well it fit the real data.

In the original publication [...]