Molecular characterization and overexpression of the difenoconazole resistance gene CYP51 in Lasiodiplodia theobromae field isolates

Stem-end rot (SER) caused by Lasiodiplodia theobromae is an important disease of mango in China. Demethylation inhibitor (DMI) fungicides are widely used for disease control in mango orchards. The baseline sensitivity to difenoconazole of 138 L. theobromae isolates collected from mango in the field in 2019 was established by the mycelial growth rate method. The cross-resistance to six site-specific fungicides with different modes of action were investigated using 20 isolates randomly selected. The possible mechanism for L. theobromae resistance to difenoconazole was preliminarily determined through gene sequence alignment and quantitative real-time PCR analysis. The results showed that the EC50 values of 138 L. theobromae isolates to difenoconazole ranged from 0.01 to 13.72 µg/mL. The frequency of difenoconazole sensitivity formed a normal distribution curve when the outliers were excluded. Difenoconazole showed positive cross-resistance only with the DMI tebuconazole but not with non-DMI fungicides carbendazim, pyraclostrobin, fludioxonil, bromothalonil, or iprodione. Some multifungicide-resistant isolates of L. theobromae were found. Two amino acid substitutions (E209k and G207A) were found in the CYP51 protein, but they were unlikely to be related to the resistance phenotype. There was no alteration in the promoter region of the CYP51 gene. However, difenoconazole significantly increased the expression of the CYP51 gene in the resistant isolates compared to the susceptible isolates. These results are vital to develop effective mango disease management strategies to avoid the development of further resistance.

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

An assessment of health information literacy among women in rural Lake Zone, Tanzania

This article presents the results from a descriptive cross-sectional survey that was conducted to assess the health information literacy skills of women of childbearing age in rural Lake Zone, Tanzania. A total of 349 women were involved in the study. The study found that most rural women in the study area have low levels of health information literacy. The aggregate scores of health information literacy indicate a mean of 42.86% with a normal distribution curve, and estimated close-to-zero skewness (0.172) and kurtosis (−0.297) measures. The causal relationships between health information literacy and women’s socio-demographic factors indicate a positive and statistically significant effect ( p < .01) of women’s level of education, income, ownership of means of communication and access to health facilities on their level of health information literacy. The women’s inadequate ability to access, read, understand, appraise and use health information is a barrier to their acquisition of relevant health information. Enhancing the health information literacy skills of these women is most likely to improve their health outcomes.

Assessment of medical and pharmaceutical waste flows during the coronavirus pandemic in the Rabat-Sale-Kenitra region, Morocco

The COVID-19 pandemic has created unprecedented difficulties for health care institutions, which are required to manage not only the flow of patients with COVID-19, but also the management of medical and pharmaceutical waste (MPW). At the level of Morocco, the waste produced by hospitals has risen sharply in the regions most affected by the virus, such as the Rabat-Sale-Kenitra region (15.05% of recorded cases). The objective of this study is to perform a descriptive statistical analysis and to evaluate the generation rates of MPW generated during the treatment of the coronavirus pandemic, with reference to a large health care hospital in the region, in order to enable decision-makers to adopt responses in terms of regular and continuous management of MPW. The Moulay Abdellah hospital in Sale has a bedding capacity allocated to the COVID-19 patient of 110 beds with a Average Occupation Rate (AOR) of 100% and an average production of 13tons per month. The study showed that the average rate of MPW generated is 4 kg per bed per day, which is twice as high as the average generation rate during normal operation in 2019. As well, frequency analysis of the data revealed that MPW generation follows a log normal distribution with a correlation coefficient of 0.9. The distribution is skewed to the right and flatter than the normal distribution curve as judged by the skewness coefficient which is 0.87 and kurtosis coefficient which has a value of 1.286, indicating a deviation from normality.

Pore Structure Characteristics and Its Effect on Mechanical Performance of Cemented Paste Backfill

The development of cemented paste backfilling (CPB) technology has made an important contribution to the mining economy. As a kind of porous material, the pore structure characteristic of cemented paste backfill (CPB) is strongly correlated to its mechanical properties. In this study, CPB specimens were prepared with tailings/cement ratios (T/C ratio) of 4, 6, 10 and curing durations of 3, 7, 14, and 28 days, respectively. Pore structures characteristics of CPB specimens were investigated using nuclear magnetic resonance (NMR) and scanning electronic microscopy (SEM). The uniaxial compressive strength (UCS) was adopted to illustrate the mechanical property of CPB specimens. The coupling effects of T/C ratio and curing time on the pore characteristics of CPB as well as the effect of pore size on the UCS were analyzed. The results indicated that: 1) the microstructural integrity of CPB was highly related to the development status of the pore structure, which can be represented by micro-parameters like porosity, average pore area, etc. 2) a similar normal distribution curve was observed from the four kinds of pore structure in CPB. As the curing time increased, the peak of the pore size curve shifted left, and the peak value decreased, which means that the pore size in CPB decreased and became much concentrated; 3) the extension of the most probable pore size led to the cross-connection of pores and resulted in the fracture of CPB, which was shown as a crack on the main section.

Molecular Characterization and Overexpression of CYP51 gene of Difenoconazole Resistance in Lasiodiplodia Theobromae Field Isolates

Stem-end rot (SER) caused by Lasiodiplodia theobromae is an important disease of mango in China. Demethylation inhibitor (DMI) fungicide are widely used for diseases control in mango orchards. The baseline sensitivity to difenoconazole of 138 isolates collected in the field in 2019 from mango were established by the mycelial growth rate method. The cross-resistance to six site-specific fungicides with different modes of action were investigated using 20 isolates randomly selected. The possible reasons for L. theobromae resistance to difenoconazole were preliminarily determined through gene sequence alignment and quantitative real-time PCR analysis. The results showed that the EC50 values of 138 L. theobromae isolates to difenoconazole ranged from 0.01 to 13.72 µg/ml. The frequency of difenoconazole sensitivity formed a normal distribution curve when the outliers were excluded. Difenoconazole showed positive cross-resistance only with the DMI tebuconazole, but not with non-DMI carbendazim, pyraclostrobin, fludioxonil, bromothalonil, or iprodione. Some multifungicide-resistant isolates of L. theobromae were found. Two amino acid substitutions (E209k and G207A) were found in CYP51 protein, but they were not likely related to the resistance phenotype. There was no alteration in promoter region of the CYP51 gene. However, difenoconazole significantly increased the expression of the CYP51 gene in the resistant isolates when compared to the susceptible isolates. This study is important references to explore resistance mechanism. These results are vital to make effective mango diseases management strategies in order to avoid the development of further resistance.

POST EXAM ANALYSIS SELECTION OF HIGH AND LOW EXAM RESULT GROUPS BY NORMAL DISTRIBUTION CURVE

The key factor of an assessment is to minimize the errors by having a good reliability and validity of the assessment yardstick. To achieve high score in the test examinee must be aware about assessment cycle and use it in appropriate way in post exam analysis. Outcome of the results can be utilized as a constructive feedback in any given program. This cross-sectional study was conducted at department of Biochemistry, University of Rajarata. Multiple choice questions, structured essay type questions, objective structured practical examination, and continuous assessment was used in this study. Total number of students are 180 and was assessed for difficulty index, discrimination index, reliability, and standard error of measurement. In this study sample for analysis was used basically the examiner divides students into two groups (‘high’ and ‘low’) according to the score sheet of each student. Most of them are doing in a wrong way basically they divide high and low clusters as 25% each and considered upper quartile and lower quartile. In this study we compared it with the standard normal distribution curve where high and low groups are considered as 16% where is the standard. There is no significant difference among both clusters, and we recommend using the standard 16% as the high and low groups in post examination analysis. Keywords: difficulty index, post examination analysis, reliability of the examination, standard error of measurement

Growth and structure of pre-mature mixed stands of Scots pine created by direct seeding in the boreal zone

The purpose of the research is to analyse the successful creation of an artificial pine forest by seeding and develop recommendations for the guaranteed reproduction of pine stands in Northern European Russia. In recent decades, there has been a steady decline in the share of pine stands and their replacement with low-value and low-yielding tree species. We surveyed 12 permanent sample plots that were laid out in various variants of forest crops. The taxation parameters were obtained by a standard analysis of the experimental data. The evaluation parameters of the stands vary within the following limits: the average diameter of the pine trees varied from 21.9 to 30.9 cm; the total basal area of the pine varied from 19.1 to 38.8 m2∙ha–1; the average height of the pine varied from 20.1 to 26.8 m; the number of growing trees varied from 754 to 1 952 ha–1; the pines varied from 382 to 762 ha–1; the growing stocks of stands varied from 416 to 608 m3∙ha–1. The distribution of pine trees by thickness steps showed that all the studied samples were close to the normal distribution curve. The results of the correlation and multidimensional analyses showed that the creation method of the forest crops had a significant impact on the value of the taxation parameters. It was found that the best options for growing pure pine stands that can be recommended for practical production are plots with a large share of soil cultivation and the size of the seedbed.

Local initiatives for green space using Poland's Village Fund: evidence from Lodzkie voivodeship

Poland's Village Fund (Fundusz Sołecki) is an instrument operating at the level of the so-called sołectwo, into which local-administration units known as gminas may be further sub-divided. These are therefore auxiliary administrative units in rural areas whose receipt of means from the Fund in question allows for the activation of local communities. Against that background, the research detailed here sought to examine Village Fund by reference to the greenspace-related projects pursued using it in the rural gminas of Łódzkie Voivodeship. Additional aspects are the classification of the tasks carried out, presentation of the statistical analysis applied, and consideration of the breakdown obtained for indicators relating to the share of funds allocated to green areas – by reference to the properties of the normal distribution curve.

A Novel Seepage Safety Monitoring Model of CFRD with Slab Cracks Using Monitoring Data

The traditional regression model usually simulates the influence of water pressure and rainfall in the early stage based on experience, but it is not suitable. To solve this problem, the normal distribution curve is used to simulate the lagging effect of water pressure and rainfall on dam seepage. In view of problem of slab cracks, the influence of cracks on seepage is analyzed. In this paper, a safety monitoring model for concrete face rockfill dam (CFRD) seepage with cracks considering the lagging effect is proposed, in which slab cracks are considered as an influencing factor. The radial basis function neural network (RBFNN) optimized by genetic algorithm (GA) is used to establish a safety monitoring model for a CFRD seepage. Seepage of the dam is predicted by this model, whose results are similar to the monitoring data, which indicates that the method has certain applicability. Through the analysis of the proportion of factors affecting CFRD seepage, it is found that the rainfall component has the greatest impact on the total seepage, accounting for more than 50%, and the crack component accounts for about 10%. Finally, through the cloud model, the monitoring index of CFRD seepage is worked out, which has certain guiding significance for the treatment of abnormal seepage monitoring data.