An ensemble multi-scale framework for long-term forecasting of air quality.

The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.

Characterization and Complete Genomic Analysis of Vibrio Parahaemolyticus-Infecting Phage KIT05.

Vibrio parahaemolyticus is a bacterial pathogen in marine aquaculture systems and a major cause of food-borne illnesses worldwide. In the present study, Vibrio phage KIT05 was isolated from water collected from a shrimp farm in the Mekong Delta, Vietnam. It was characterized based on its morphology, growth curve, lytic properties, and genome sequence. Under the electron microscope, KIT05 particles had an icosahedral head with a diameter of 62.3 nm and a short tail of 24.1 nm. The one-step growth curve of KIT05 showed that its latency time was approximately 40 min and burst size was 18 plaque-forming units/cell. The genome of KIT05 comprises 50,628 bp with a GC content of 41.63%. It contains 60 open reading frames that are encoded within both strands and four tRNAs. The presence of direct terminal repeats of 130 bp at both ends of the KIT05 DNA was determined. According to phage morphology, genomic organization, and phylogeny analysis, Vibrio phage KIT05 was classified into the family Podoviridae. The genome annotation revealed that KIT05 had no virulent or lysogenic genes. This study may help identify a novel candidate for developing biocontrol agents for Vibrio parahaemolyticus.

Assessment of the distribution and ecological risks of heavy metals in coastal sediments in Vietnam's Mong Cai area.

Coastal sediments in the Mong Cai area were collected and analyzed for grain size, heavy metals, total organic carbon, and isotopes (210Pb, 226Ra, δ15N, δ13C) to assess sediment quality. The most common sediments were fine sand in surface sediment, very fine sand in core C1, and very coarse and coarse silt in core C2. The total organic carbon was highest in C2 next to the surface and lowest in C1, with content levels of 1.81%, 0.40%, and 0.31%, respectively. The chronology in C1 was 1877-2019 (142 years, 0-5 0 cm), with an average sedimentation rate of 0.71 cm/year. In C2, the chronology was 1944-2019 (75 years, 0-14 cm), with an average sedimentation rate of 0.27 cm/year. These δ13C and δ15N in the sediment reflect the source of the organic matter mix from the marine and terrigenous sediments. All studied heavy metals were lower than the ISQGs, with the exception of As in C1 and C2, which were higher. C1 showed a decline in As over time, while C2 As levels increased between 1996 and 2019. In terms of heavy metal pollution indexes, the geoaccumulation index (Igeo) showed that C1 and C2 were unpolluted to moderately polluted with As, with Li and Pb in C2; the enrichment factor (EF) was moderately enriched with As; the contamination factor (CF) was moderately contaminated (Pb, Cd, Fe, Mo, and Li) in C2 and C1 (Cd, As, Li) and considerably contaminated (As) in C2. The risk factor (ER) of As showed a moderate potential ecological risk in C2. The degree of contamination (CD) ranged from moderate to considerable (C1, C2), and the ecological risk (RI) was low. Although CD ranged from moderate (C1) to considerable (C2), most contamination was concentrated at the bottom of the cores. RI was low. The Mong Cai sediment quality does not currently affect the coastal area's ecosystem and fauna.

Multi-term time fractional diffusion equations and novel parameter estimation techniques for chloride ions sub-diffusion in reinforced concrete.

In this paper, searching for a better chloride ions sub-diffusion system, a multi-term time-fractional derivative diffusion model is proposed for the description of the time-dependent chloride ions penetration in reinforced concrete structures exposed to chloride environments. We prove the stability and convergence of the model. We use the modified grid approximation method (MGAM) to estimate the fractional orders and chloride ions diffusion coefficients in the reinforced concrete for the multi-term time fractional diffusion system. And then to verify the efficiency and accuracy of the proposed methods in dealing with the fractional inverse problem, two numerical examples with real data are investigated. Meanwhile, we use two methods of fixed chloride ions diffusion coefficient and variable diffusion coefficient with diffusion depth to simulate chloride ions sub-diffusion system. The result shows that with the new fractional orders and parameters, our multi-term fractional order chloride ions sub-diffusion system is capable of providing numerical results that agree better with the real data than other models. On the other hand, it is also noticed from the numerical solution of the chloride ions sub-diffusion system that setting the variable diffusion coefficient with diffusion depth is more reasonable. And it is also found that chloride ions diffusion coefficients in reinforced concrete should be decreased with diffusion depth which is completely consistent with the theory. In addition, the model can be used to predict the chloride profiles with a time-dependent property. This article is part of the theme issue 'Advanced materials modelling via fractional calculus: challenges and perspectives'.

Multifractal temporally weighted detrended cross-correlation analysis of multivariate time series.

Fractal and multifractal properties of various systems have been studied extensively. In this paper, first, the multivariate multifractal detrend cross-correlation analysis (MMXDFA) is proposed to investigate the multifractal features in multivariate time series. MMXDFA may produce oscillations in the fluctuation function and spurious cross correlations. In order to overcome these problems, we then propose the multivariate multifractal temporally weighted detrended cross-correlation analysis (MMTWXDFA). In relation to the multivariate detrended cross-correlation analysis and multifractal temporally weighted detrended cross-correlation analysis, an innovation of MMTWXDFA is the application of the signed Manhattan distance to calculate the local detrended covariance function. To evaluate the performance of the MMXDFA and MMTWXDFA methods, we apply them on some artificially generated multivariate series. Several numerical tests demonstrate that both methods can identify their fractality, but MMTWXDFA can detect long-range cross correlations and simultaneously quantify the levels of cross correlation between two multivariate series more accurately.

Physical activity and sedentary behaviour during pregnancy are associated with gestational weight gain in Vietnamese women.

BACKGROUND AND OBJECTIVES: Gestational weight gain is known to impact maternal and child health outcomes. Energy intake and energy expenditure are major components of clinical nutrition in relation to weight gain during pregnancy. The study was to determine the association of physical activity and sitting time during pregnancy with gestational weight gain in Vietnamese women. METHODS AND STUDY DESIGN: A multicentre prospective cohort study was conducted in Vietnam from 2015 to 2017. A total of 1873 women with a singleton pregnancy were included. Physical activity and sitting exposures during pregnancy were determined using an interviewer-administered validated questionnaire. Multiple regression analysis was performed to assess physical activity and sitting time in relation to gestational weight gain, adjusting for the confounding effects of maternal characteristics and total energy intake during pregnancy. RESULTS: The mean weight gain was 12.9 (Standard deviation 4.1) kg throughout pregnancy. Pregnant women with prolonged sitting time gained an average of 0.6 kg more weight (p=0.016 for highest versus lowest tertiles). Conversely, women who were physically active, in terms of having higher tertiles of total physical activity, moderate-to-vigorous-intensity, household/ caregiving activities, and occupational physical activity, experienced significantly less gestational weight gain (p<0.05 for highest versus lowest tertiles). CONCLUSIONS: Inverse associations were found between gestational weight gain and physical activity (i.e. intensities and several domains), whereas gestational weight gain tended to increase with longer sitting time. Therefore, being physically active and less sedentary is important to regulate weight gain during pregnancy.

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method.

HIV-1 viruses, which are predominant in the family of HIV viruses, have strong pathogenicity and infectivity. They can evolve into many different variants in a very short time. In this study, we propose a new and effective alignment-free method for the phylogenetic analysis of HIV-1 viruses using complete genome sequences. Our method combines the position distribution information and the counts of the k-mers together. We also propose a metric to determine the optimal k value. We name our method the Position-Weighted k-mers (PWkmer) method. Validation and comparison with the Robinson-Foulds distance method and the modified bootstrap method on a benchmark dataset show that our method is reliable for the phylogenetic analysis of HIV-1 viruses. PWkmer can resolve within-group variations for different known subtypes of Group M of HIV-1 viruses. This method is simple and computationally fast for whole genome phylogenetic analysis.

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs.

BACKGROUND: Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS: We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred .

Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets.

A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series.

Whole genome/proteome based phylogeny reconstruction for prokaryotes using higher order Markov model and chaos game representation.

UNLABELLED: Traditional methods for sequence comparison and phylogeny reconstruction rely on pair wise and multiple sequence alignments. But alignment could not be directly applied to whole genome/proteome comparison and phylogenomic studies due to their high computational complexity. Hence alignment-free methods became popular in recent years. Here we propose a fast alignment-free method for whole genome/proteome comparison and phylogeny reconstruction using higher order Markov model and chaos game representation. In the present method, we use the transition matrices of higher order Markov models to characterize amino acid or DNA sequences for their comparison. The order of the Markov model is uniquely identified by maximizing the average Shannon entropy of conditional probability distributions. Using one-dimensional chaos game representation and linked list, this method can reduce large memory and time consumption which is due to the large-scale conditional probability distributions. To illustrate the effectiveness of our method, we employ it for fast phylogeny reconstruction based on genome/proteome sequences of two species data sets used in previous published papers. Our results demonstrate that the present method is useful and efficient. AVAILABILITY AND IMPLEMENTATION: The source codes for our algorithm to get the distance matrix and genome/proteome sequences can be downloaded from ftp://121.199.20.25/. The software Phylip and EvolView we used to construct phylogenetic trees can be referred from their websites.

Whole-proteome based phylogenetic tree construction with inter-amino-acid distances and the conditional geometric distribution profiles.

There has been a growing interest in alignment-free methods for whole genome comparison and phylogenomic studies. In this study, we propose an alignment-free method for phylogenetic tree construction using whole-proteome sequences. Based on the inter-amino-acid distances, we first convert the whole-proteome sequences into inter-amino-acid distance vectors, which are called observed inter-amino-acid distance profiles. Then, we propose to use conditional geometric distribution profiles (the distributions of sequences where the amino acids are placed randomly and independently) as the reference distribution profiles. Last the relative deviation between the observed and reference distribution profiles is used to define a simple metric that reflects the phylogenetic relationships between whole-proteome sequences of different organisms. We name our method inter-amino-acid distances and conditional geometric distribution profiles (IAGDP). We evaluate our method on two data sets: the benchmark dataset including 29 genomes used in previous published papers, and another one including 67 mammal genomes. Our results demonstrate that the new method is useful and efficient.

Determination of multifractal dimensions of complex networks by means of the sandbox algorithm.

Complex networks have attracted much attention in diverse areas of science and technology. Multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we employ the sandbox (SB) algorithm proposed by Tél et al. (Physica A 159, 155-166 (1989)), for MFA of complex networks. First, we compare the SB algorithm with two existing algorithms of MFA for complex networks: the compact-box-burning algorithm proposed by Furuya and Yakubo (Phys. Rev. E 84, 036118 (2011)), and the improved box-counting algorithm proposed by Li et al. (J. Stat. Mech.: Theor. Exp. 2014, P02020 (2014)) by calculating the mass exponents τ(q) of some deterministic model networks. We make a detailed comparison between the numerical and theoretical results of these model networks. The comparison results show that the SB algorithm is the most effective and feasible algorithm to calculate the mass exponents τ(q) and to explore the multifractal behavior of complex networks. Then, we apply the SB algorithm to study the multifractal property of some classic model networks, such as scale-free networks, small-world networks, and random networks. Our results show that multifractality exists in scale-free networks, that of small-world networks is not obvious, and it almost does not exist in random networks.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC.

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php.

Photocatalytic degradation of methylene blue under visible light by cobalt ferrite nanoparticles/graphene quantum dots.

A simple approach was developed to synthesize cobalt ferrite nanoparticles/graphene quantum dots (CF/GQDs). The material was prepared from a homogeneous mixture of iron nitrate, cobalt nitrate, and starch at 140, 180 and 200 °C in a 24 h thermal hydrolysis process. The obtained materials were characterised by using X-ray diffraction, scanning electron microscopy, transmission electron microscopy, ultraviolet-visible diffuse reflectance spectroscopy, Fourier-transform infrared spectroscopy, photoluminescence spectroscopy, vibrating-sample magnetometry, and nitrogen adsorption/desorption isotherms. Cobalt ferrite crystals of around 8-10 nm and graphene quantum dots formed directly at 200 °C. Stacking GQDs sheets onto the CF nanoparticles resulted in CF/GQDs nanoparticles. The nanocomposite exhibits satisfactory fluorescent and superparamagnetic properties, which are vital for catalytic applications. The CF/GQDs catalyse significantly the degradation of methylene blue (MB) under visible light. The catalyst can be recycled with an external magnetic field and displays suitable stability. Also, it was reused in three successive experiments with a loss of efficiency of about 5%. The CF/GQDs are considered as an efficient photocatalyst for MB degradation and other dyes.

Development of pH-responsive Eudragit S100-functionalized silk fibroin nanoparticles as a prospective drug delivery system.

Silk fibroin nanoparticles (FNP) have been increasingly investigated in biomedical fields due to their biocompatibility and biodegradability properties. To widen the FNP versatility and applications, and to control the drug release from the FNP, this study developed the Eudragit S100-functionalized FNP (ES100-FNP) as a pH-responsive drug delivery system, by two distinct methods of co-condensation and adsorption, employing the zwitterionic furosemide as a model drug. The particles were characterized by sizes and zeta potentials (DLS method), morphology (electron microscopy), drug entrapment efficiency and release profiles (UV-Vis spectroscopy), and chemical structures (FT-IR, XRD, and DSC). The ES100-FNP possessed nano-sizes of ∼200-350 nm, zeta potentials of ∼ -20 mV, silk-II structures, enhanced thermo-stability, non-cytotoxic to the erythrocytes, and drug entrapment efficiencies of 30%-60%, dependent on the formulation processes. Interestingly, the co-condensation method yielded the smooth spherical particles, whereas the adsorption method resulted in durian-shaped ones due to furosemide re-crystallization. The ES100-FNP adsorbed furosemide via physical adsorption, followed Langmuir model and pseudo-second-order kinetics. In the simulated oral condition, the particles could protect the drug in the stomach (pH 1.2), and gradually released the drug in the intestine (pH 6.8). Remarkably, in different pH conditions of 6.8, 9.5, and 12, the ES100-FNP could control the furosemide release rates depending on the formulation methods. The ES100-FNP made by the co-condensation method was mainly controlled by the swelling and corrosion process of ES100, and followed the Korsmeyer-Peppas non-Fickian transport mechanism. Whereas, the ES100-FNP made by the adsorption method showed constant release rates, followed the zero-order kinetics, due to the gradual furosemide dissolution in the media. Conclusively, the ES100-FNP demonstrated high versatility as a pH-responsive drug delivery system for biomedical applications.

Transformations for the compression of FASTQ quality scores of next-generation sequencing data.

MOTIVATION: The growth of next-generation sequencing means that more effective and efficient archiving methods are needed to store the generated data for public dissemination and in anticipation of more mature analytical methods later. This article examines methods for compressing the quality score component of the data to partly address this problem. RESULTS: We compare several compression policies for quality scores, in terms of both compression effectiveness and overall efficiency. The policies employ lossy and lossless transformations with one of several coding schemes. Experiments show that both lossy and lossless transformations are useful, and that simple coding methods, which consume less computing resources, are highly competitive, especially when random access to reads is needed. AVAILABILITY AND IMPLEMENTATION: Our C++ implementation, released under the Lesser General Public License, is available for download at http://www.cb.k.u-tokyo.ac.jp/asailab/members/rwan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fabrication and characterization of a NiO-ZnO/PANI-CNTs composite for sensing of methanol in an aqueous environment.

In this study, we fabricated a composite of NiO-ZnO/PANI-CNTs on a fluorine tin oxide (FTO) electrode and examined the electrochemical sensing behavior of the modified electrode to detect methanol in aqueous solution. The structural, morphological, and electrochemical properties of the composite were characterized using various methods such as X-ray diffraction (XRD), EDS, FTIR, X-ray photoelectron spectroscopy (XPS), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), selected area electron diffraction (SAED), and electrochemical techniques such as cyclic voltammetry (CV) and chronoamperometry (CA). The composite-based electrode showed excellent electrocatalytic activity and selectivity for methanol oxidation. The calibration equation obtained was ΔI = 0.0003 × CMeOH + 0.02811, with a high correlation coefficient of 0.9993, over a wide detection range of 0 to 500 mM. The material exhibits great potential for the fabrication of sensors to detect methanol in commercial products. Real gasoline samples have been selected to evaluate the practical performance and feasibility of this as-prepared sensor. The experimental data indicated that the recovery of gasoline samples is about 98%, indicating this to be an appropriate detection procedure for effective electrochemical determination of MeOH in real gasoline samples.

Chemical compositions and biological activities of Serevenia buxifolia essential oil leaves cultivated in Vietnam (Thua Thien Hue).

Serevenia buxifolia is an evergreen citrus plant and has attracted considerable attention due to its bioactive components and biological activities. In the present study, the essential oil (EO) from S. buxifolia cultivated in Vietnam was demonstrated to exhibit the in vitro antioxidant, thrombolytic, anti-hemolysis, anti-inflammatory, and antidiabetic activities. Briefly, the gas chromatography coupled to mass spectrometry analysis revealed that the leaf EO of S. buxifolia was composed of 33 components, with the main constituents being ß-carypphyllene (32.5%), and elixene (9.8%). The extracted oil possessed a fairly high free radical scavenging activity against 2, 2-diphenyl-1-picrylhydrazyl (DPPH), with an IC50 value of 190.7 µg/mL compared with positive control, α-tocopherol, IC50 value of 42.6 µg/mL. The EO also exhibited thrombolytic activity: the percentage of inhibition was found to be 70.75% at 100 µL, in comparison with 87.2% for the positive control, streptokinase. For hemolytic activity, the percentage of inhibition of the EO was from 27.4% to 59.6% at concentrations from 10 to 100 µg/mL, respectively. The results of in vitro anti-inflammatory activity indicated that the EO of S. buxifolia leaves effectively protects the heat-induced denaturation, with an IC50 value of 40.25 µg/mL. The EO also exhibited antidiabetic potential, with IC50 values of 87.8 and 134.9 µg/mL against α-amylase and α-glucosidase, respectively. It is noteworthy that the potent biological activities of the obtained S. buxifolia oil increased in a dose-dependent manner. The results achieved show that the EO of S. buxifolia leaves can be a potential source for oxidative stress, inflammatory, and diabetic management.

Stable biogenic silver nanoparticles from Syzygium nervosum bud extract for enhanced catalytic, antibacterial and antifungal properties.

In the present study, the biosynthesis of stable silver nanoparticles (BioAgNPs) was accomplished successfully for the first time by using an aqueous extract derived from the buds of Syzygium nervosum (SN) as both a reducing and a stabilizing agent. Transmission electron microscopy (TEM) and high-resolution transmission electron microscopy (HR-TEM) investigations revealed that the biosynthesized BioAgNPs were predominantly spherical with an average size of 10-30 nm. It was found that the outstanding stability of the BioAgNPs colloidal solution was assigned to the additive effect of the surrounding protective organic layer and the highly negatively charged surface of the nanoparticles. Consequently, good antibacterial activity was demonstrated by the colloidal BioAgNPs solution against four distinct bacterial strains, including Gram-positive S. aureus and B. subtilis as well as Gram-negative E. coli and S. typhi. Interestingly, the biosynthesized BioAgNPs displayed greater antibacterial activity even when tested at low doses against Gram-negative S. typhi. In addition, the biogenic AgNPs demonstrated a significant level of catalytic activity in the process of converting 2-NP, 3-NP, and 4-NP into aminophenols within 15 min, with reaction rate constants of 9.0 × 10-4, 10 × 10-4, and 9.0 × 10-4 s-1, respectively. BioAgNPs formulations were assessed against anthracnose disease in tea plants and were found to be as effective as the positive control at a dose of 20-fold dilution, but less effective at a dose of 30-fold dilution. Both doses of BioAgNPs formulations significantly suppressed Colletotrichum camelliae (anthracnose disease) without affecting the growth of the tea plants.

Assessing cognitive decline in Vietnamese older adults using the Montreal Cognitive Assessment-Basic (MoCA-B) and Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) during the COVID-19 pandemic: A feasibility study.

Objectives: The lack of cognitive assessment tools suitable for people with minimal formal education is a barrier to identify cognitive impairment in Vietnam. Our aims were to (i) evaluate the feasibility of conducting the Montreal Cognitive Assessment-Basic (MoCA-B) and Informant Questionnaire On Cognitive Decline in the Elderly (IQCODE) remotely on the Vietnamese older adults, (ii) examine the association between the two tests, (iii) identify demographic factors correlated with these tools. Methods: The MoCA-B was adapted from the original English version, and a remote testing procedure was conducted. One hundred seventy-three participants aged 60 and above living in the Vietnamese southern provinces were recruited via an online platform during the COVID-19 pandemic. Results: IQCODE results showed that the proportions of rural participants classified as having mild cognitive impairment and dementia were substantially higher than those in urban areas. Levels of education and living areas were associated with IQCODE scores. Education attainment was also the main predictor of MoCA-B scores (30% of variance explained), with an average of 10.5 points difference between those with no formal education and those who attended university. Conclusions: It is feasible to administer the IQCODE and MoCA-B remotely in the Vietnamese older population. Education attainment played a stronger role in predicting MoCA-B scores than IQCODE, suggesting the influence of this factor on MoCA-B scores. Further study is needed to develop socio-culturally appropriate cognitive screening tests for the Vietnamese population.