Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of Elicitation Conditions to Enhance the Production of Potent Metabolite Withanolide from Withania somnifera (L.)

This study aimed at optimizing conditions for increased withanolide production in Withania somnifera. The elicitors used for the foliar spray on the aerial parts of the plant were salicylic acid, jasmonic acid, and chitosan for the enhancement of withanolides in Withania somnifera under different environmental regimes. Three different elicitors, i.e., chitosan, jasmonic acid and salicylic acid, were applied on the plants through foliar route every 15th day for 6 months, and later plants were used for sample preparation. Further, the elicitors were used in different concentration, i.e., jasmonic acid (50, 200 and 400 ppm), chitosan (10, 50 and 100 ppm) and salicylic acid (0.5, 1 and 2 ppm). The elicitors were sprayed on the foliar parts of the plant between 10:00–11:00 a.m. on application days. For elicitor spray, a calibrated sprayer was used. The withanolide A/withaferin A was quantified through HPLC. It was found that in an open environment, maximum withaferin A content, i.e., 0.570 mg/g (DW), was recorded with jasmonic acid (50 ppm) treatment in comparison to control (0.067 mg/g DW). Thus, there was an 8.5-fold increase in the withaferin A content. Maximum withanolide A content of 0.352 mg/g (DW) was recorded when chitosan (50 ppm) was sprayed, while in the control, withanolide A content was recorded to be 0.031 mg/g (DW); thus, chitosan application increased the production of withanolide A by 11.3-fold. Under controlled conditions, maximum withaferin A content of 1.659 mg/g (DW) was recorded when plants were sprayed with chitosan (100 ppm), which was 8.1 times greater than the control content of 0.203 mg/g (DW). Maximum withanolide A content of 0.460 mg/g (DW) was recorded when chitosan (100 ppm) was applied, whereas in the control, withanolide A content was found to be 0.061 mg/g (DW). Thus, foliar spraying of elicitors in very low concentrations can serve as a low-cost, eco-friendly, labor-intensive and elegant alternative approach that can be practiced by farmers for the enhancement, consistent production and improved yield of withanolide A/withaferin A. This can be a suitable way to enhance plant productivity, thus increasing the availability of withanolide A and withaferin A for the health and pharma industry.
Pig Coat Color Manipulation by MC1R Gene Editing

Black coat color in pigs is determined by the dominant E allele at the MC1R locus. Through comparing MC1R gene sequences between recessive e and dominant ED1 alleles, we identified four missense mutations that could affect MC1R protein function for eumelanin synthesis. With the aim of devising a genetic modification method for pig coat color manipulation, we mutated the e allele in the Duroc breed to the dominant ED1 allele using CRISPR-mediated homologous recombination for the four mutation substitutions at the MC1R locus. The MC1R-modified Duroc pigs generated using the allele replacement strategy displayed uniform black coat color across the body. A genotyping assay showed that the MC1R-modified Duroc pigs had a heterozygous ED1/e allele at the MC1R locus; in addition, the pigs remained in the Duroc genetic background. Our work offers a gene editing method for pig coat color manipulation, which could value the culture of new pig varieties meeting the needs of diversified market.
Watershed Segmentation of Cervical Images Using Multiscale Morphological Gradient and HSI Color Space

In this paper, a novel watershed segmentation algorithm is proposed for the segmentation of nucleus from the surrounding cytoplasm of cervical cancer images. The proposed method converts the input RGB image into HSI image which contains three components hue, saturation and intensity. The saturation component is thresholded to obtain the binary image and each pixel in the binary image is multiplied with hue component to obtain the product image. The intensity image is complemented, thresholded and merged with the product image and smoothened. The local minima are reduced using extended minima function and the multiscale gradient of this resultant gray scale image is segmented using watershed algorithm based on Hill Climbing technique. Experimental results add to the computational efficiency of the algorithm, its shape maintaining, edge preserving and scale-calibrating features. The performance is also superior to most other segmentation techniques.
The Effect of Authentic Leadership of Deans and Directors on Sustainable Organizational Commitment at Universities: Mediated by Organizational Culture and Trust

This study aimed to analyze the relationship between deans and directors of Korean universities on the organizational commitment of university professors, and the mediating effects of organizational culture and organizational trust. A survey was conducted with full-time professors at 20 universities; 387 responses were analyzed. Structural equation modeling was performed using a two-step approach to achieve the research purpose, and the mediating effects were tested using phantom variables. The analysis revealed that the authentic leadership of deans and directors significantly and positively affected organizational trust, culture, and commitment, with trust being a more important mediator than culture. This study is significant in that most previous studies on university leadership have focused on the university president, the top-level policymaker, and used measurement tools designed for business organizations. Conversely, this study focused on deans and directors, the middle managers at universities, and adapted existing diagnostic tools for universities in Korea with horizontal organizational structures based on academic experience. In Korea, there is currently an emphasis on university reform, evaluation, and financial support. Thus, the importance of deans and directors as the key administrative workforce and enablers in creating a healthy organizational culture is in the spotlight The results suggest the urgent necessity for investment in leadership training for these positions.
Investigating the Relationship between High School Technology Education and Test Scores for Algebra 1 and Geometry

This Article is brought to you for free and open access by the STEM Education & Professional Studies at ODU Digital Commons. It has been accepted for inclusion in STEMPS Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact digitalcommons@odu.edu. Repository Citation Dyer, Richard R. ; Reed, Philip A. ; and Berry, Robert Q. , "Investigating the Relationship Between High School Technology Education and Test Scores for Algebra 1 and Geometry" (2006). STEMPS Faculty Publications. 30. http://digitalcommons.odu.edu/stemps_fac_pubs/30
Time Perspective and Experience of Depression, Stress, and Loneliness Among Adolescents in Youth Educational Centres

Time perspective is of key significance in overcoming an identity crisis in adolescence. Research using the Zimbardo Time Perspective Inventory (ZTPI) was conducted on a group of 311 adolescents (aged 13–18) in several youth educational centres in south-eastern Poland. The research was designed to identify the significance of time perspective when it comes to levels of depression, stress and loneliness experienced in conditions of institutional rehabilitation and education. Linear regression analysis demonstrated the following: a positive past focus reduces the intensity of anxiety and depression; a positive past and future focus results in reduced depression, loneliness and stress; the experience of stress combined with depressionenhances the significance of time perspective for the feeling of loneliness; and a low level of family loneliness reduces depression.
A Visual–Audio-Based Emotion Recognition System Integrating Dimensional Analysis

Dimensional emotion recognition research is an important branch of affective computing, which uses continuous values to represent complex human emotions. In this study, we propose a visual–audio emotion recognition system that integrated emotional dimensions. For the visual part, the corresponding relationship between emotion category and emotion dimension interval is established based on rules, and the respective classifiers are trained and fused using the machine learning methods. For the audio part, some emotion-related features are extracted, and a 128-D global feature is extracted through a deep convolutional neural network (DCNN). We use a combination of Bayesian and machine learning to integrate the information of visual–audio modalities. We have tested the proposed system and its single modalities on the standard databases CK+ and eNTERFACE ’05, and the experimental results and comparison showed the efficiency of the proposed system. Furthermore, our proposed system uses emotion category label and dimension values simultaneously to represent emotion, providing strong interpretability and expansibility for emotion recognition, which goes beyond the methods only come with either classification or dimension.
Wafer defect inspection using component tree of SEM images

This paper proposes a novel defect detection method using component tree of SEM images. The component tree contains rich information about the topological structure of images such as stiffness of intensity changes, area, and volume of lobes. The information is effective in detecting suspicious spot of the defects. A quasi-linear algorithm for constructing the component tree and computing those features is available. This paper modifies the original component tree algorithm to be suitable to the defect detection. One idea is excluding the pixels near the ground level in the initial stage of constructing the component tree. The other idea is to detect significant lobes by using both the volume and area attributes. The experiments performed with actual SEM images and printed electronics images showed promising results. For a 1000*1000 image, the proposed algorithm ran whole process in 1.36 seconds.
Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

Lung cancer is the fastest-growing and most dangerous type of cancer worldwide. It ranks first among cancer diseases in the number of deaths, and diagnosing it at late stages makes treatment more difficult. Artificial intelligence has played an essential role in the medical field in general, and early diagnosis of diseases and analyzing medical images in particular, as it can reduce human errors that may occur with the medical expert in medical image analysis. In this study, a hybrid framework is proposed between deep learning using the proposed convolutional neural network and multi-criteria decision-making techniques in order to reach an effective and accurate classification model for lung cancer diagnosis and select the best methodology to solve the problem of class imbalance datasets, which is a general problem in medical data that causes problems and errors in prediction. The IQ-OTHNCCD dataset that has a class imbalance was used. Three class balancing techniques were used separately and the data from each one enters the proposed convolutional neural network for feature extraction and classification. Then the Fuzzy-Weighted Zero-Inconsistency algorithm and VIKOR were used to make the ranking for the best classification approach and determine the best technique to balance the classes. This contributed to increasing the efficiency of the classification, where the best model got an accuracy of 99.27 %, sensitivity of 99.33 %, specificity of 99 %, precision of 98.67 % and F1-score of 99 %. This study can be applied to any data that suffers from the class imbalance problem to find the best technique that gives the highest classification accuracy.
Physicochemical Properties and Surface Characteristics of Ground Human Teeth

Enamel, dentin and cementum apatite has a complex composition. The lack of complete reports on the chemical composition of all tooth tissues together and the need to create a modern biomaterial that reproduces the correct ratio of individual tooth mineral components prompted the authors to undertake the research. A detailed evaluation of the micro- and macro-elements of tooth powder, using various methods of chemical analysis was conducted. All four groups of human sound teeth were crushed using the grinder. A fine powder was implemented for the FTIR (Fourier Transform Infrared Spectroscopy), ICP (Inductively Coupled Plasma Optical Emission Spectometry) and for the potentiometric titration, SEM and mercury porosimetry analyses. The obtained studies indicate that there is no significant correlation in chemical composition between the different teeth types. This proves that every removed, crushed tooth free of microorganisms can be a suitable material for alveolar augmentation. It is essential to know the chemical profiles of different elements in teeth to develop a new class of biomaterials for clinical applications.
Statistical theory of perturbation waves in transport phenomena and its experimental verification

In transport phenomena, perturbation waves are a result of interaction of molecules in gases and liquids, charged particles (ions, electrons) in plasma, conduction electrons and phonons in solid bodies. General statistical theory of the perturbation waves is developed and its corollaries are studied. On this basis is proved universality of introduced earlier local time concept, which leads to a formulation of kinetic, conservation and governing equations for macroscopic transport phenomena with finite speed of the perturbations propagation in gases, liquids, solids and plasma. Speed of thermal perturbations propagation in phonon and Fermi electron gases and plasma, and also speed of thermal, momentum and mass perturbations propagation in ideal gas are theoretically determined. It is shown that published experimental results for femtosecond laser heating of thin gold films and results of power modulation experiments in JET tokamak agree with the developed theory.
SSMD: Dimensionality Reduction and Classification of Hyperspectral Images Based on Spatial–Spectral Manifold Distance Metric Learning

Metric learning, which aims to obtain a metric matrix M such that samples from the same class are close to one another and samples of different classes are far from one another, is widely used in the field of hyperspectral dimensionality reduction (DR) and classification. Traditional metric learning is based on the Mahalanobis distance, which measures the similarity between samples via point-to-point distance, ignoring the structural features of the hyperspectral images (HSIs). To solve the above problem, we proposed clustered multiple manifold metric learning (CM3L), which obtains a manifold distance (MD), aimed at improving discrimination by introducing structural features of the HSIs and achieving good results. However, this manifold distance still has certain shortcomings in specific application situations. MD only considers the labeled data in the construction of the manifold and ignores the unlabeled data, resulting in the destruction of the manifold. Therefore, this article proposes a new spatial–spectral manifold distance (SSMD) to improve the performance of metric learning in hyperspectral DR and classification by maintaining the integrity of the constructed manifolds. The SSMD selects suitable neighboring points in the labeled and unlabeled data through the spectral–spatial information in order to participate in the construction of the manifold. Then, the distance between the manifolds is calculated to replace the traditional Mahalanobis distance. The results of seven sets of comparison experiments on three real HSI datasets demonstrate the effectiveness of SSMD in improving the classification results of HSIs.
Facebook and mosquito-borne disease outbreaks: An analysis of public responses to federal health agencies’ posts about dengue and Zika in 2016

Responses of Facebook users to four United States federal health agencies’ social media posts about dengue and Zika (mosquito-borne diseases), surveillance, and control during the Zika outbreak in 2016 were tracked. Official Facebook pages of health agencies were analyzed, and a qualitative analysis program was used to perform a thematic analysis of the data on public responses to health agency posts. Public sentiment analysis showed that Facebook users had a negative sentiment towards health information observed in this study. Themes were identified in the studied posts, giving insight into the nature of public discussions and responses to federal health agencies. Themes were assessed based on the way the agencies’ mosquito-borne disease information was received by the public through the social media platform, Facebook. Results indicate that public perception/understanding of mosquito-borne disease outbreaks can be assessed by analyzing public interactions with health agencies on Facebook. The importance of maximizing effectiveness by addressing issues in sharing health education information, risk communication, and monitoring of public responses by health agencies through social media platforms is discussed.
Unveiling undergraduate production engineering students' comprehension of process flow measures

Paper aims: This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process. Originality: This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures. Research method: This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size). The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units. Main findings: These results show that students perceived the use of resource pooling as an impactful practice. However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled. Implications for theory and practice: The teaching of basic concepts of OM requires the support of computational tools. Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning.
The Neurotoxic Effect of Ochratoxin-A on the Hippocampal Neurogenic Niche of Adult Mouse Brain

Ochratoxin A (OTA) is a common secondary metabolite of Aspergillus ochraceus, A. carbonarius, and Penicillium verrucosum. This mycotoxin is largely present as a contaminant in several cereal crops and human foodstuffs, including grapes, corn, nuts, and figs, among others. Preclinical studies have reported the involvement of OTA in metabolic, physiologic, and immunologic disturbances as well as in carcinogenesis. More recently, it has also been suggested that OTA may impair hippocampal neurogenesis in vivo and that this might be associated with learning and memory deficits. Furthermore, aside from its widely proven toxicity in tissues other than the brain, there is reason to believe that OTA contributes to neurodegenerative disorders. Thus, in this present in vivo study, we investigated this possibility by intraperitoneally (i.p.) administering 3.5 mg OTA/kg body weight to adult male mice to assess whether chronic exposure to this mycotoxin negatively affects cell viability in the dentate gyrus of the hippocampus. Immunohistochemistry assays showed that doses of 3.5 mg/kg caused a significant and dose-dependent reduction in repetitive cell division and branching (from 12% to 62%). Moreover, the number of countable astrocytes (p < 0.001), young neurons (p < 0.001), and mature neurons (p < 0.001) negatively correlated with the number of i.p. OTA injections administered (one, two, three, or six repeated doses). Our results show that OTA induced adverse effects in the hippocampus cells of adult mice brain tissue when administered in cumulative doses.
ACTIVE MEDIA. LASERS: Influence of small oxygen additions on the small-signal-gain dynamics in the active medium of a pulsed electron-beam-controlled discharge CO laser

The method for measuring the rate constants of the VV' exchange between CO and O2 molecules is developed. The method is based on a comparison of the measured and numerically calculated small-signal-gain (SSG) dynamics in the active medium of a pulsed electron-beam-controlled cryogenic CO laser. The SSG dynamics was measured in CO—He—O2 mixtures with the fixed CO:He = 1:4 ratio by varying the oxygen content from 0 to 4%. The SSG dynamics was measured by probing the active medium of the pulsed CO laser by a beam from a tunable cw CO laser. The mathematical model of the CO laser takes into account the influence of oxygen on energy exchange processes. The parameters of the analytic approximation for constants of the near-resonance VV' exchange CO(v)+O2(u=0) → CO(v-1)+O2(u=1) are determined by the developed method for quantum vibrational numbers v = 18—24. In particular, the rate constant for v = 20 is (3.1±0.5)×10-12 cm3 s-1 at the gas temperature of 150 K. The extrapolation of the obtained analytic expression for rate constants in gases at room temperature gives the reasonable agreement with the measurements performed earlier for v = 12 and 13.
Pore Space Deformation and Its Implications for Two‐Phase Flow Through Porous Media: A Micro‐Scale Experimental Investigation

Pore space deformation may significantly affect the behavior of multiphase flow through porous media, leading to changes in the effective permeability, relative permeability, and fluid distributions. However, due to the lack of fundamental studies on the mechanisms correlating the pore‐level fluid displacements with the pore space deformation, no compelling conclusions have been reached to account for the observed effects. To explore this correlation, we performed a series of two‐phase (oil/brine) flow experiments on a miniature water‐wet sandpack under various stress conditions, while imaging the pore space and pore fluid configurations using high‐resolution x‐ray microtomography. We investigated the impacts of pore space contraction on the frequency of different pore‐scale fluid displacement mechanisms, as well as the subsequent changes in the fluid saturation and distribution during the drainage and imbibition processes. Variations in the in‐situ capillary pressure with pore space contraction were also examined to further interpret the impacts on fluid displacements. Furthermore, we predicted changes in the relative permeabilities by integrating the end‐point relative permeabilities computed through direct simulations and the end‐point saturations measured from x‐ray images. Our results reveal that the impacts of the pore space deformation on the occupancies of the wetting and non‐wetting phases were distinct. Additionally, the hydraulic conductivity to the wetting phase was more sensitive to the pore space deformation than that to the non‐wetting phase because of their topologically different distributions in the pore space. Consequently, after compression, the relative permeability of the wetting phase decreased, while that of the non‐wetting phase increased.