Strong Invasive Mechanism of Wedelia trilobata via Growth and Physiological Traits under Nitrogen Stress Condition.

Nitrogen (N) is one of the most crucial elements for plant growth. However, a deficiency of N affects plant growth and development. Wedelia trilobata is a notorious invasive plant species that exhibits superior tolerance to adapt to environmental stresses. Yet, research on the growth and antioxidant defensive system of invasive Wedelia under low N stress, which could contribute to understanding invasion mechanisms, is still limited. Therefore, this study aims to investigate and compare the tolerance capability of invasive and native Wedelia under low and normal N conditions. Native and invasive Wedelia species were grown in normal and low-N conditions using a hydroponic nutrient solution for 8 weeks to assess the photosynthetic parameters, antioxidant activity, and localization of reactive oxygen species (ROS). The growth and biomass of W. trilobata were significantly (p < 0.05) higher than W. chinensis under low N. The leaves of W. trilobata resulted in a significant increase in chlorophyll a, chlorophyll b, and total chlorophyll content by 40.2, 56.2, and 46%, respectively, compared with W. chinensis. W. trilobata significantly enhanced antioxidant defense systems through catalase, peroxidase, and superoxide dismutase by 18.6%, 20%, and 36.3%, respectively, providing a positive response to oxidative stress caused by low N. The PCA analysis showed that W. trilobata was 95.3% correlated with physiological traits by Dim1 (79.1%) and Dim2 (16.3%). This study provides positive feedback on W. trilobata with respect to its comprehensive invasion mechanism to improve agricultural systems via eco-friendly approaches in N deficit conditions, thereby contributing to the reclamation of barren land.

Genomic overview of INA-induced NPR1 targeting and transcriptional cascades in Arabidopsis.

The phytohormone salicylic acid (SA) triggers transcriptional reprogramming that leads to SA-induced immunity in plants. NPR1 is an SA receptor and master transcriptional regulator in SA-triggered transcriptional reprogramming. Despite the indispensable role of NPR1, genome-wide direct targets of NPR1 specific to SA signaling have not been identified. Here, we report INA (functional SA analog)-specific genome-wide targets of Arabidopsis NPR1 in plants expressing GFP-fused NPR1 under its native promoter. Analyses of NPR1-dependently expressed direct NPR1 targets revealed that NPR1 primarily activates genes encoding transcription factors upon INA treatment, triggering transcriptional cascades required for INA-induced transcriptional reprogramming and immunity. We identified genome-wide targets of a histone acetyltransferase, HAC1, including hundreds of co-targets shared with NPR1, and showed that NPR1 and HAC1 regulate INA-induced histone acetylation and expression of a subset of the co-targets. Genomic NPR1 targeting was principally mediated by TGACG-motif binding protein (TGA) transcription factors. Furthermore, a group of NPR1 targets mostly encoding transcriptional regulators was already bound to NPR1 in the basal state and showed more rapid and robust induction than other NPR1 targets upon SA signaling. Thus, our study unveils genome-wide NPR1 targeting, its role in transcriptional reprogramming, and the cooperativity between NPR1, HAC1, and TGAs in INA-induced immunity.

Invasive Wedelia trilobata Performs Better Than Its Native Congener in Various Forms of Phosphorous in Different Growth Stages.

At present, many hypotheses have been proposed to explain the mechanism of alien plants' successful invasion; the resource fluctuations hypothesis indicates that nutrient availability is a main abiotic factor driving the invasion of alien plants. Higher phosphorus utilization and absorption efficiency might be one of the important reasons for alien plants successful invasion. Wedelia trilobata, one of the notorious invasive weeds in China, possesses a strong ability to continue their development under infertile habitats. In this study, firstly, W. trilobata and its native congener, W. chinensis, were grown in various phosphorus forms to test their absorption efficiency of phosphorus. Secondly, the different responses of W. trilobata and W. chinensis to the insoluble phosphorus in three growth stages (at 30, 60, and 150 days cultivation) were also tested. The results showed that the growth rate, root morphology, and phosphorus absorption efficiency of W. trilobata under various insoluble, organic, or low phosphorus conditions were significantly higher than that of W. chinensis. During the short-term cultivation period (30 d), the growth of W. trilobata under insoluble and low phosphorus treatments had no significant difference, and the growth of W. trilobata in insoluble phosphorus treatment also had no significant effect in long-term cultivation (60 and 150 d). However, the growth of W. chinensis in each period under the conditions of insoluble and low phosphorus was significantly inhibited throughout these three growth stages. Therefore, invasive W. trilobata had a higher phosphorus utilization efficiency than its native congener. This study could explain how invasive W. trilobata performs under nutrient-poor habitats, while also providing favorable evidence for the resource fluctuations hypothesis.

Dose dependent effect of nitrogen on the phyto extractability of Cd in metal contaminated soil using Wedelia trilobata.

Cadmium (Cd) is one of the toxic heavy metal that negatively affect plant growth and compromise food safety for human consumption. Nitrogen (N) is an essential macronutrient for plant growth and development. It may enhance Cd tolerance of invasive plant species by maintaining biochemical and physiological characteristics during phytoextraction of Cd. A comparative study was conducted to evaluate the phenotypical and physiological responses of invasive W. trilobata and native W. chinensis under low Cd (10 µM) and high Cd (80 µM) stress, along with different N levels (i.e., normal 91.05 mg kg-1 and low 0.9105 mg kg-1). Under low-N and Cd stress, the growth of leaves, stem and roots in W. trilobata was significantly increased by 35-23%, 25-28%, and 35-35%, respectively, compared to W. chinensis. Wedelia trilobata exhibited heightened antioxidant activities of catalase and peroxidase were significantly increased under Cd stress to alleviate oxidative stress. Similarly, flavonoid content was significantly increased by 40-50% in W. trilobata to promote Cd tolerance via activation of the secondary metabolites. An adverse effect of Cd in the leaves of W. chinensis was further verified by a novel hyperspectral imaging technology in the form of normalized differential vegetation index (NDVI) and photochemical reflectance index (PRI) compared to W. trilobata. Additionally, W. trilobata increased the Cd tolerance by regulating Cd accumulation in the shoots and roots, bolstering its potential for phytoextraction potential. This study demonstrated that W. trilobata positively responds to Cd with enhanced growth and antioxidant capabilities, providing a new platform for phytoremediation in agricultural lands to protect the environment from heavy metals pollution.

Plant growth promoting rhizobacteria and biochar production from Parthenium hysterophorus enhance seed germination and productivity in barley under drought stress.

Drought stress can significantly affect plant growth and development. Biochar (BC) and plant growth-promoting rhizobacteria (PGPR) have been found to increase plant fertility and development under drought conditions. The single effects of BC and PGPR in different plant species have been widely reported under abiotic stress. However, there have been relatively few studies on the positive role of PGPR, BC, and their combination in barley (Hordeum vulgare L.). Therefore, the current study investigated the effects of BC from Parthenium hysterophorus, drought tolerant PGPR (Serratia odorifera), and the combination of BC + PGPR on the growth, physiology, and biochemical traits of barley plants under drought stress for two weeks. A total of 15 pots were used under five treatments. Each pot of 4 kg soil comprised the control (T0, 90% water), drought stress alone (T1, 30% water), 35 mL PGPR/kg soil (T2, 30% water), 2.5%/kg soil BC (T3, 30% water), and a combination of BC and PGPR (T4, 30% water). Combined PGPR and BC strongly mitigated the negative effects of drought by improving the shoot length (37.03%), fresh biomass (52%), dry biomass (62.5%), and seed germination (40%) compared to the control. The PGPR + BC amendment treatment enhanced physiological traits, such as chlorophyll a (27.9%), chlorophyll b (35.3%), and total chlorophyll (31.1%), compared to the control. Similarly, the synergistic role of PGPR and BC significantly (p< 0.05) enhanced the antioxidant enzyme activity including peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD) to alleviate the toxicity of ROS. The physicochemical properties (N, K, P, and EL) of the soils were also enhanced by (85%, 33%, 52%, and 58%) respectively, under the BC + PGPR treatment compared to the control and drought stress alone. The findings of this study have suggested that the addition of BC, PGPR, and a combination of both will improve the soil fertility, productivity, and antioxidant defense systems of barley under drought stress. Therefore, BC from the invasive plant P. hysterophorus and PGPR can be applied to water-deficient areas to improve barley crop production.

Lutetium-177 Prostate Specific Membrane Antigen Therapy in a Patient With Double Malignancy and Single Functioning Kidney: A Case Report.

Lutetium-177 labeled with 617 types of Prostate Specific Membrane Antigen (177Lu PSMA-617) Radio-ligand Therapy (RLT) is an emerging modality of choice for the treatment of metastatic castration-resistant prostate carcinoma (mCRPC). After it is administered intravenously, it is excreted primarily through the kidneys. Physiological excretion and concomitant expression of PSMA receptors on renal tissues are associated with potential renal toxicity, a matter of concern while treating patients with multiple doses of RLT. There are published articles that have demonstrated the safe use of 177Lu PSMA-617 in patients with bilateral fair-functioning kidneys; however, only a single study has been published that has evaluated its safety in patients with solitary-functioning kidneys. The uniqueness of this case report lies in the fact that we have documented the renal safety profile of 177Lu PSMA-617 therapy after multiple doses in a patient who presented with double malignancy (metastatic castration-resistant prostate carcinoma and left renal cell carcinoma) and had a single-functioning right kidney.

A Green Approach Used for Heavy Metals 'Phytoremediation' Via Invasive Plant Species to Mitigate Environmental Pollution: A Review.

Heavy metals (HMs) normally occur in nature and are rapidly released into ecosystems by anthropogenic activities, leading to a series of threats to plant productivity as well as human health. Phytoremediation is a clean, eco-friendly, and cost-effective method for reducing soil toxicity, particularly in weedy plants (invasive plant species (IPS)). This method provides a favorable tool for HM hyperaccumulation using invasive plants. Improving the phytoremediation strategy requires a profound knowledge of HM uptake and translocation as well as the development of resistance or tolerance to HMs. This review describes a comprehensive mechanism of uptake and translocation of HMs and their subsequent detoxification with the IPS via phytoremediation. Additionally, the improvement of phytoremediation through advanced biotechnological strategies, including genetic engineering, nanoparticles, microorganisms, CRISPR-Cas9, and protein basis, is discussed. In summary, this appraisal will provide a new platform for the uptake, translocation, and detoxification of HMs via the phytoremediation process of the IPS.

Thermo-neutrophilic cellulases and chitinases characterized from a novel putative antifungal biocontrol agent: Bacillus subtilis TD11.

Cellulose and chitin are the most abundant naturally occurring biopolymers synthesized in plants and animals and are used for synthesis of different organic compounds and acids in the industry. Therefore, cellulases and chitinases are important for their multiple uses in industry and biotechnology. Moreover, chitinases have a role in the biological control of phytopathogens. A bacterial strain Bacillus subtilis TD11 was previously isolated and characterized as a putative biocontrol agent owing to its significant antifungal potential. In this study, cellulase and chitinase produced by the strain B. subtilis TD11 were purified and characterized. The activity of the cellulases and chitinases were optimized at different pH (2 to 10) and temperatures (20 to 90°C). The substrate specificity of cellulases was evaluated using different substances including carboxymethyl cellulose (CMC), hydroxyethyl cellulose (HEC), and crystalline substrates. The cellulase produced by B. subtilis TD11 had a molecular mass of 45 kDa while that of chitinase was 55 kDa. The optimal activities of the enzymes were found at neutral pH (6.0 to 7.0). The optimum temperature for the purified cellulases was in the range of 50 to 70°C while, purified chitinases were optimally active at 50°C. The highest substrate specificity of the purified cellulase was found for CMC (100%) followed by HEC (>50% activity) while no hydrolysis was observed against the crystalline substrates. Moreover, it was observed that the purified chitinase was inhibitory against the fungi containing chitin in their hyphal walls i.e., Rhizoctonia, Colletotrichum, Aspergillus and Fusarium having a dose-effect relationship.

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost).

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

The soil pH and heavy metals revealed their impact on soil microbial community.

Soil microbial community is the main indicator having a crucial role in the remediation of polluted soils. These microbes can alter soil pH, organic matter in soils (SOM), soil physic-chemical properties, and potential soil respiration rate via their enzymatic activities. Similarly, heavy metals also have a crucial role in soil enzymatic activities. For this purpose, a number of methods are studied to evaluate the impact of soil pH (a key factor in the formation of biogeographic microbial patterns in bacteria) on bacterial diversity. The effects of pH on microbial activity are glamorous but still unclear. Whereas, some studies also indicate that soil pH alone is not the single key player in the diversity of soil bacteria. Ecological stability is achieved in a pollution-free environment and pH value. The pH factor has a significant impact on the dynamics of microbes' communities. Here, we try to discuss factors that directly or indirectly affect soil pH and the impact of pH on microbial activity. It is also discussed the environmental factors that contribute to establishing a specific bacterial community structure that must be determined. From this, it can be concluded that the environmental impact on soil pH, reducing soil pH and interaction with this factor, and reducing the effect of soil pH on soil microbial community.

Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET.

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity.

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.

A fetal ultrasound (US) is a technique to examine a baby's maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother's or child's health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine.

Bacillus altitudinis HNH7 and Bacillus velezensis HNH9 promote plant growth through upregulation of growth-promoting genes in upland cotton.

AIMS: The potential of endophytic Bacillus strains to improve plant growth and yield was evaluated. METHODS AND RESULTS: Endophytic Bacillus altitudinis HNH7 and Bacillus velezensis HNH9 were evaluated for their growth-promoting traits. In an in vitro plate assay, HNH7 and HNH9 exhibited proteolytic, amylolytic, lipolytic and cellulolytic activity. HNH7 and HNH9 were able to solubilize iron by producing siderophores but were unable to solubilize insoluble phosphate. PCR confirmed the presence of four growth-promoting genes viz. pvd, budA, asbA and satA in the genome of HNH7, while HNH9 also possessed the same genes except for budA. In a greenhouse experiment, HNH7 and HNH9 promoted the growth of upland cotton plants by upregulating the expression of growth-linked genes, EXP6, ARF1, ARF18, IAA9, CKX6 and GID1b. However, the expression of genes involved in ethylene biosynthesis, that is ERF and ERF17 was downregulated after treating the plants with HNH7 and HNH9 compared to the control. Furthermore, cotton plants treated with HNH7 and HNH9 exhibited a significantly higher rate of photosynthesis and stomatal conductance. CONCLUSION: HNH7 and HNH9 showed a promising potential to promote the growth of cotton plants. SIGNIFICANCE AND IMPACT OF STUDY: Research on plant growth-promoting Bacillus strains can lead to the formation of biofertilizers.

Non-Expresser of PR-Genes 1 Positively Regulates Abscisic Acid Signaling in Arabidopsis thaliana.

The plant hormone, abscisic acid (ABA), is not only important for promoting abiotic stress responses but also plays a versatile and crucial role in plant immunity. The pathogen infection-induced dynamic accumulation of ABA mediates the degradation of non-expresser of PR genes 1 (NPR1) through the CUL3NPR3NPR4 proteasome pathway. However, the functional significance of NPR1 degradation by other E3 ligases in response to ABA remains unclear. Here, we report that NPR1 is induced transcriptionally by ABA and that npr1-1 mutation results in ABA insensitivity during seed germination and seedling growth. Mutants lacking NPR1 downregulate the expression of ABA-responsive transcription factors ABA INSENSITIVE4 (ABI4) and ABA INSENSITIVE5 (ABI5), and that of their downstream targets EM6, RAB18, RD26, and RD29B. The npr1-1 mutation also affects the transcriptional activity of WRKY18, which activates WRKY60 in the presence of ABA. Furthermore, NPR1 directly interacts with and is degraded by HOS15, a substrate receptor for the DDB1-CUL4 ubiquitin E3 ligase complex. Collectively, our findings demonstrate that NPR1 acts as a positive regulator of ABA-responsive genes, whereas HOS15 promotes NPR1 degradation in a proteasome-dependent manner.

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

Comparative Study of Chinese and American Media Reports on the COVID-19 and Expressions of Social Responsibility: A Critical Discourse Analysis.

Critical discourse analysis aims to explore the dialectical relationship between discourse and ideology. Based on psycholinguistic research, this paper analyzes the Chinese and American media's news reports and comments on the COVID-19. It aims to expose the hidden psychological messages and ideologies behind the words. The corpus in this paper is mainly from the official media of China Daily and Time from December 2019 to January 2021 in China and the United States. This paper uses Wang Zhenhua's Appraisal Theory and Halliday's Systemic Functional Grammar as tools to make a comparative analysis of the corpus. At the textual level, languages are classified and lexical choices are analyzed followed by the analysis of the reporter's ideology after reviewing the motivation of the reporters of two countries. On the level of social responsibility expression and discourse, the paper analyzes the news reports, which are characterized by the combination of the reporter's views on the news. In the aspect of social practice, the social and cultural factors and background of news reports are analyzed. China calls for strengthening cooperation and exchanges with other countries to jointly fight the epidemic. The Chinese government has actively shared its experience and made corresponding contributions to international economic recovery. However, the US government shirks its responsibility by claiming that the effective implementation of Chinese methods and experience in China does not mean that it can achieve corresponding results in Europe and the US. At the same time, the United States provides medical supplies to other countries. This study hopes to help awaken readers' critical thinking and increase their awareness of the anti-control of mass discourse. At the same time, it is hoped that readers can view the epidemic from a more scientific perspective, understand the facts and reject the unwarranted panic. It will also help reshape Chinese and American discourse.

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.

Leadership empowering behaviour as a predictor of employees' psychological well-being: Evidence from a cross-sectional study among secondary school teachers in Kohat Division, Pakistan.

In this technologically developed scenario, many organizations in developing countries including Pakistan have expanded the enthusiasm for understanding and creating an encouraging administrative and managerial environment. Numerous organizations are struggling for structural changes by deserting the old-fashioned organizational management style and implementing an empowering leadership where leaders give more authority to subordinates in decision making and responsibilities with the aim to increase organizational productivity. Therefore, the study examined the leadership empowering behaviour as a predictor of employees' psychological well-being of the educational institutions at secondary level in Kohat Division, Pakistan. A total sample of 564 secondary school teachers (male n = 379; female n = 185) was carefully chosen through a stratified random sampling technique. In this study, a non-experimental predictive correlational design was adopted. In order to collect data from the participants, two different standardized research tools i.e., the Leader Empowering Behaviour Questionnaire and Ryff's Psychological Well-being Scale were used. After the collection of data, it was analyzed on the basis of mean, standard deviation, Pearson's product-moment correlation, and multiple linear regression model. In conclusion, the study confirmed a significant positive correlation between leadership empowering behaviour and employees' psychological well-being. Leadership empowering behaviours predict employees' psychological well-being positively. Therefore, it was recommended that empowering behaviour might be adopted by the school leaders to improve the employees' psychological well-being for better organizational productivity.