Effective drying processes for Taikor (Garcinia pedunculata Roxb.) fruit by ultrasound-assisted osmotic pretreatment: Analysis of quality and kinetic models.

The present study aimed to analyze and establish an effective combination of ultrasound and immersion pretreatment processes for drying Taikor (Garcinia pendunculata Roxb.) fruits. Taikorslices were first immersed in 10 % sucrose, fructose, and glucose solution. Then, the immersed slices were treated in an ultrasonic bath at 30 °C for 10, 20, and 30 min. Drying operations were carried out at 50, 60, and 70 °C, with a fixed relative humidity of 30 %. The Page, Newton, Henderson and Pabis, and Weibull distribution models were fitted to the obtained drying data to determine the best kinetic model that effectively describes the drying properties ofTaikor. After drying operations, changes in quality parameters, e.g., ß-carotene, vitamin C, B vitamins, color, antioxidant activities, and microbial loads, were measured to obtain the best drying temperature and the most effective pretreatment combination with minimum loss of nutrients of the sample. Among different kinetic models, both Page and Weibull distribution models showed the best R2 values of 0.9867 and 0.9366, respectively. The chemical properties were preserved to the greatest extent possible by drying at 50 °C with glucose pretreatment. The color parameters were better preserved by fructose pretreatment. Sonication time also had profound effect on the quality parameters of dried Taikor slices. However, higher temperature drying required a shorter time for drying and exhibited better performance in microbial load reduction. This study's findings will help to establish an effective drying condition forGarcinia pedunculatafruits.

Microwave and ultrasound-assisted extraction of bioactive compounds from Papaya: A sustainable green process.

The demand for sustainable and eco-friendly extraction methods for bioactive compounds from natural sources has increased significantly in recent years. In this study, we investigated the effectiveness of the microwave pretreated ultrasound-assisted extraction (MPUAE) process for the extraction of antioxidants (TPC, DPPH, and FRAP) from papaya pulp and peel. The optimized variables for the MPUAE process were determined using the Box-Behnken design tool of response surface methodology. Our results showed that the optimized variables for pulp and peel were 675.76 and 669.70 W microwave power, 150 s of irradiation time, 30 °C ultrasound temperature, and 19.70 and 16.46 min of ultrasonic extraction time, respectively. Moreover, the MPUAE process was found to be more energy-efficient and environmentally friendly compared to the conventional ultrasound-associated extraction (UAE) technique. The MPUAE process emitted less CO2 to the environment and had a shorter extraction time, resulting in a more sustainable and cost-effective extraction process. Our study suggests that the MPUAE process has the potential to be a promising and eco-friendly alternative for the industrial extraction of bioactive compounds from papaya and other natural sources.

Functional data analysis to characterize disease patterns in frequent longitudinal data: application to bacterial vaginal microbiota patterns using weekly Nugent scores and identification of pattern-specific risk factors.

BACKGROUND: Technology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups compared to traditional longitudinal studies where the number of follow-up is often small. Such data allow explorations of the role of intra-person variability in understanding disease etiology and characterizing disease processes. A specific example was to characterize pathogenesis of bacterial vaginosis (BV) using weekly vaginal microbiota Nugent assay scores collected over 2 years in post-menarcheeal women from Rakai, Uganda, and to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV. METHODS: We use a fully data-driven approach to characterize the longitudinal patters of vaginal microbiota by considering the densely sampled Nugent scores to be random functions over time and performing dimension reduction by functional principal components. Extending a current functional data clustering method, we use a hierarchical functional clustering framework considering multiple data features to help identify clinically meaningful patterns of vaginal microbiota fluctuations. Additionally, multinomial logistic regression was used to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV. RESULTS: Using weekly Nugent scores over 2 years of 211 sexually active and post-menarcheal women in Rakai, four patterns of vaginal microbiota variation were identified: persistent with a BV state (high Nugent scores), persistent with normal ranged Nugent scores, large fluctuation of Nugent scores which however are predominantly in the BV state; large fluctuation of Nugent scores but predominantly the scores are in the normal state. Higher Nugent score at the start of an interval, younger age group of less than 20 years, unprotected source for bathing water, a woman's partner's being not circumcised, use of injectable/Norplant hormonal contraceptives for family planning were associated with higher odds of persistent BV in women. CONCLUSION: The hierarchical functional data clustering method can be used for fully data driven unsupervised clustering of densely sampled longitudinal data to identify clinically informative clusters and risk-factors associated with each cluster.

Benchmarking the Effects of Water Demand Management During Water Crisis in a Regional City.

Regional cities are having their unique water security challenges due to regional urban water contexts, regional socio-economic structure, and climate conditions. Regional urban community's perceptions of water usage are expected to be different from the communities in large metropolitan cities. The city of Townsville is the largest regional city in the northern tropical region of the state of Queensland in Australia, and it is known to have its unique dry tropic climate condition. The city faced a water crisis due to a prolonged drought in 2013-2018. As part of this research, at first, a literature review was conducted to understand what water demand management (WDM) tools worked well during urban water crisis in different parts of the world. This paper then investigates how residential water usage changed with the changes in drought measures in the city of Townsville in the last decade. A minimum per capita residential water requirement is established for the study region to benchmark the effects of tools implemented in the region. The paper investigates the WDM policies implemented in the city of Townsville including when the policies were applied and the impacts and efficacy of these policies before water crisis, during water crisis and after water crisis. The most effective WDM tools identified are water restrictions, public awareness raising and education programmes. The impacts of water restriction policies and the perceptions of local water professionals on their success elements are also studied. The results are compared and the reasons behind the findings are investigated.

Adaptation to climate change: A study on regional climate change adaptation policy and practice framework.

Although planning and policy instruments are important for climate change adaptation, the implementation of these measures is critical for success. This paper studies different climate change adaptation strategies by analysing the measures adopted by stakeholders in charge of government policy development and implementation to minimise the impacts of climate change in the northern tropical region of Queensland, Australia. Local government organisations are responsible for taking a leading role in climate change adaptation. State and commonwealth government agencies are primarily responsible for developing climate transition policies and guidelines, as well as providing limited financial aid to help support the local government. Interviews were conducted with local government practitioners identified from different local government authorities in the study region. Although all the government bodies made some progress in developing better climate change adaptation policies, the interview participants identified that a lot more needs to be done, especially in implementation, including devising and the application of relevant action plans, economic assessments, stakeholder participations and engagement. From a local government practitioners' viewpoint, both the water sector and local economy will face the highest immediate impacts if climate change adaptation actions are not adequately implemented at local government level in the study region. There are currently no notable legal bindings to address climate change risks in the region. In addition, financial liability assessments due to climate risks and cost-share mechanisms among different levels of stakeholders and government authorities to face and prepare for climate change impacts hardly exist. Although the interview respondents recognise their high importance. As there are uncertainties in the achievements of climate change adaptation plans, from a local government practitioners' standpoint, the local authorities should take appropriate actions to integrate adaptation and mitigation works to face and prepare for climate risks rather than focusing only on adaptation. The respondents informed that some work has been done to identify flood prone areas and a few policy documents exist that accommodate sea level rise in planning practice, but these are done in fragments with no holistic implementation, monitoring or evaluation plans put in place.

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data.

Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

Application of nanotechnology in food: processing, preservation, packaging and safety assessment.

Even though nanotechnology is extensively applied in agriculture, biochemistry, medicine and many other sectors, it is a developing field that conforms to new and more complex applications in food systems as compared to other technologies. It offers a viable strategy for integrating cutting-edge technology into a wide range of operations related to the production, development, fabrication, packaging, storage and distribution of food. The most fundamentally sophisticated technology in nano-based food science, nanoparticles deal with a wide range of nanostructured materials and nano methods, including nanofood, nanotubes, nanocomposites, nano packaging, nanocapsules, nanosensors, liposomes, nanoemulsions, polymeric nanoparticles and nanoencapsulation. This method is developed to increase food solubility and shelf life, availability of bioactive chemical, the protection of food constituents, nutritional supplementation, fortification and food or constituent delivery. Additionally, it serves as an antibacterial agent by generating reactive oxygen species (ROS) which cause bacterial DNA damage, protein denaturation and cell damage. Although the use of nanotechnology in food applications is advancing, there are certain negative or dangerous effects on health related to the toxicity and dangers of ingesting nanoparticles in food. The use of nanotechnology in the food industry, notably in processing, preservation and packaging, with its promising future, was addressed in this study. The toxicity of nanoparticles in food as well as its development in food safety assessments with certain areas of concern were also reviewed.

Statistical perspective on functional and causal neural connectomics: The Time-Aware PC algorithm.

The representation of the flow of information between neurons in the brain based on their activity is termed the causal functional connectome. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the directed probabilistic graphical modeling. Its common formulation is not suitable for neural time series since it was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the Time-Aware PC (TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm-a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the directed Markov property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.

PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes.

In order to forecast the axial load-carrying capacity of concrete-filled steel tubular (CFST) columns using principal component analysis (PCA), this work compares hybrid models of artificial neural networks (ANNs) and meta-heuristic optimization algorithms (MOAs). In order to create hybrid ANN models, a dataset of 149 experimental tests was initially gathered from the accessible literature. Eight PCA-based hybrid ANNs were created using eight MOAs, including artificial bee colony, ant lion optimization, biogeography-based optimization, differential evolution, genetic algorithm, grey wolf optimizer, moth flame optimization and particle swarm optimization. The created ANNs' performance was then assessed. With R2 ranges between 0.7094 and 0.9667 in the training phase and between 0.6883 and 0.9634 in the testing phase, we discovered that the accuracy of the built hybrid models was good. Based on the outcomes of the experiments, the generated ANN-GWO (hybrid model of ANN and grey wolf optimizer) produced the most accurate predictions in the training and testing phases, respectively, with R2 = 0.9667 and 0.9634. The created ANN-GWO may be utilised as a substitute tool to estimate the load-carrying capacity of CFST columns in civil engineering projects according to the experimental findings.

Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete.

The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R2 (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R2 of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL.

A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms.

The current work presents a comparative study of hybrid models that use support vector machines (SVMs) and meta-heuristic optimization algorithms (MOAs) to predict the ultimate interfacial bond strength (IBS) capacity of fiber-reinforced polymer (FRP). More precisely, a dataset containing 136 experimental tests was first collected from the available literature for the development of hybrid SVM models. Five MOAs, namely the particle swarm optimization, the grey wolf optimizer, the equilibrium optimizer, the Harris hawks optimization and the slime mold algorithm, were used; five hybrid SVMs were constructed. The performance of the developed SVMs was then evaluated. The accuracy of the constructed hybrid models was found to be on the higher side, with R2 ranges between 0.8870 and 0.9774 in the training phase and between 0.8270 and 0.9294 in the testing phase. Based on the experimental results, the developed SVM-HHO (a hybrid model that uses an SVM and the Harris hawks optimization) was overall the most accurate model, with R2 values of 0.9241 and 0.9241 in the training and testing phases, respectively. Experimental results also demonstrate that the developed hybrid SVM can be used as an alternate tool for estimating the ultimate IBS capacity of FRP concrete in civil engineering projects.

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study.

Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property-an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.

Application of immersion pre-treatments and drying temperatures to improve the comprehensive quality of pineapple (Ananas comosus) slices.

Drying of pineapple slices combined with different pre-treatments was done to reduce various adverse changes by adding satisfactory value. Process optimization was done by dipping the pineapple slices in four different solutions (1% trehalose, 2% NaCl, 10% sucrose, and 10% fructose) before drying. The effects of different pre-treatments and drying temperatures of 50, 55, and 60 °C with a constant 30% relative humidity (RH) were optimized based on the quality attributes, drying time and microbial load of dried pineapple slices. The optimal drying temperature was 55 °C using 1% trehalose pre-treatments based on the physical and biochemical properties. The reconstituted dried pineapples implied at this condition, contributed to the better structure preservation as indicated by the lower shrinkage (0.21) and the higher Coefficient of Rehydration (0.941), and rehydration ratio (6.840). On the other hand, the retention of color, vitamin C, B vitamins, and antioxidant activity of the samples were decreased by increasing drying time and temperatures. The highest Total Phenolic Content (121.02 mg GAE/100g), Total Flavonoid Content (8.72 mg QE/100g), and DPPH radical scavenging activity (7.22 EC50 g/100g) were found at 60 °C drying temperature with 10% fructose pretreatment's samples. The lowest drying time required was 7.64 h using 2% NaCl pre-treatment at 60 °C, considering the time required to reach 20% moisture content in the dried product at 30% RH. Based on the reported results, it is concluded that 1% trehalose at 50 °C can be used to develop high quality pineapple snacks, which maintained the maximum desired physicochemical and nutritious properties. This study could play an essential role in meeting the emerging demand of developing good quality nutritious dried pineapple snacks.

Seroprevalence of Herpes Simplex Virus Infection in HIV Coinfected Individuals in Eastern India with Risk Factor Analysis.

Herpes simplex virus type 2 (HSV-2) is the cause of most genital herpes while HSV-1 is responsible for orolabial and facial lesions. In immunocompromised individuals, like HIV patients, impaired immunity leads to more frequent symptomatic and asymptomatic HSV infection. Fifty-two blood samples from HIV patients with clinically diagnosed HSV infection were taken as cases, while 45 blood samples each from HIV-infected (HIV control) and noninfected patients without any herpetic lesion (non-HIV control) were taken as control. Serum was tested for IgM and IgG antibodies of both HSV-1 and HSV-2 by ELISA. The seroprevalence was compared among the three groups of study population, considering the demographic and socioeconomic parameters. The HSV-2 IgM was significantly higher (p < 0.005) in the HIV patient group (34.6%) than the HIV control (2.2%) and non-HIV control (2.2%) groups, whereas HSV-2 IgG seroprevalence was higher in both HIV patient (61.5%) and HIV control (57.8%) groups than the non-HIV control group (17.8%). The prevalence of HSV-2 was significantly higher in persons with multiple partners and in the reproductive age group. The overall seroprevalence of HSV-1 IgM was too low (<5%), whereas it was too high (about 90%) with HSV-1 IgG in all three study groups.

Peri-operative anaesthetic management of neonates for surgical emergencies.

Surgery of neonates can be fraught with hazards unless the anaesthesiologist is conversant with the physiologic and pharmacologic responses under anaesthesia and surgery. The principles of anaesthetic management for surgery of critically ill neonates with common congenital malformations are briefly discussed in this article.

Perioperative management of pregnant patients with heart disease for caesarian section under anaesthesia.

One hundred pregnant patients, of age group 22 to 35 years, with different types of cardiac ailments (mitral stenosis, mitral regurgitation, mitral valve prolapse, aortic regurgitation, atrial septal defect, ventricular septal defect, coarctation of the aorta, Eisenmenger syndrome, hypertrophic obstructive cardiomyopathy and operated tetralogy of fallot), put up for elective caesarian section underanaesthesia, were managed in the department of anaesthesiology at IPGME&R/SSKM Hospital, Kolkata from January 1996 to December 2002. The aim of the study was to observe the maternal and foetal outcome in different heart diseases. Death occurred in 2 patients (67%) with Eisenmenger syndrome, in one patient (20%) with hypertrophic obstructive cardiomyopathy and in one patient (5%) with critical mitral stenosis (mitral orifice area = 0.6 cm2) with pulmonary arterial hypertension (PAH). Neonatal mortality was observed in 4 patients [Eisenmenger syndrome--3 (100%); coarctation of the aorta--1 (33%)]. Another 8 patients developed severe heart failure (HF) [severe mitral stenosis (mitral orifice area = 1-1.2 cm2)--2 (10%); hypertrophic obstructive cardiomyopathy--4 (80%); coarctation of the aorta--2 (66%)]. Foetal dysmaturity was observed in 20 neonates (54%) belonging to mothers of New York Heart Association (NYHA) classes III and IV. Congenital heart disease (ventricular septal defect) was detected in 3 offsprings (20%) of mothers with ventricular septal defect. The study concludes that most pregnant cardiac patients can have a satisfactory outcome with careful perioperative management.