Combined effect of endophytic Bacillus mycoides and rock phosphate on the amelioration of heavy metal stress in wheat plants.

BACKGROUND: Zinc (Zn) and nickel (Ni) are nutrients that are crucial for plant growth; however, when they are present at higher concentrations, they can cause toxicity in plants. The present study aimed to isolate plant growth promoting endophytic bacteria from Viburnum grandiflorum and assess its plant and defense promoting potential alone and in combination with RP in zinc (Zn) and nickel (Ni) toxic soil. The isolated endophytic bacteria were identified using 16s rRNA gene sequencing. For the experiment, twelve different treatments were applied using Zn, Ni, isolated endophytic Bacillus mycoides (Accession # MW979613), and rock phosphate (RP). The Ni, Zn and RP were used at the rate of (100 mg/kg) and (0.2 g/kg) respectively. A pot experiment with three replicates of each treatment was conducted using a complete randomized design (CRD). RESULTS: The results indicated that Ni (T5 = seed + 100 mg/kg Ni and T9 = seed + 100 mg/kg Zn) and Zn concentrations inhibited plant growth, but the intensity of growth inhibition was higher in Ni-contaminated soil. Bacillus mycoides and RP at 100 mg/Kg Zn (T12 = inoculated seed + 100 mg/kg Zn + RP0.2 g/kg.) increased the shoot length, leaf width, protein and sugar content by 57%, 13%, 20% and 34%, respectively, compared to the control. The antioxidant enzymes superoxide dismutases (SOD), peroxidase (POD) were decreased in contaminated soil. Furthermore, Ni and Zn accumulation was inhibited in T11 (seed + 100 mg/kg Zn + RP0.2 g/Kg) and T12 (inoculated seed + 100 mg/kg Zn + RP0.2 g/Kg) by 62 and 63% respectively. The Cu, Ca, and K, contents increased by 128, 219 and 85, Mn, Na, and K by 326, 449, and 84% in (T3 = inoculated seed) and (T4 = inoculated seed + RP 0.2 g/Kg) respectively. CONCLUSIONS: Ni was more toxic to plants than Zn, but endophytic bacteria isolated from Viburnum grandiflorum, helped wheat (Triticum aestivum) plants and reduced the toxic effects of Ni and Zn. The effect of Bacillus mycoides was more prominent in combination with RP which promoted and suppressed heavy-metal toxicity. The reported combination of Bacillus mycoides and RP may be useful for improving plant growth and overcoming metal stress.

Temporal profiling of physiological, histological, and transcriptomic dissection during auxin-induced adventitious root formation in tetraploid Robinia pseudoacacia micro-cuttings.

MAIN CONCLUSION: Optimal levels of indole-3-butyric acid (IBA) applied at the stem base promote adventitious root (AR) initiation and primordia formation, thus promoting the rooting of leafy micro-cuttings of tetraploid Robinia pseudoacacia. Tetraploid Robinia pseudoacacia L. is a widely cultivated tree in most regions of China that has a hard-rooting capability, propagated by stem cuttings. This study utilizes histological, physiological, and transcriptomic approaches to explore how root primordia are induced after indole butyric acid (IBA) treatment of micro-cuttings. IBA application promoted cell divisions in some cells within the vasculature, showing subcellular features associated with adventitious root (AR) founder cells. The anatomical structure explicitly showed that AR initiated from the cambium layer and instigate the inducible development of AR primordia. Meanwhile, the hormone data showed that similar to that of indole-3-acetic acid, the contents of trans-zeatin and abscisic acid peaked at early stages of AR formation and increased gradually in primordia formation across the subsequent stages, suggesting their indispensable roles in AR induction. On the contrary, 24-epibrassinolide roughly maintained at extremely high levels during primordium initiation thoroughly, indicating its presence was involved in cell-specific reorganization during AR development. Furthermore, antioxidant activities transiently increased in the basal region of micro-cuttings and may serve as biochemical indicators for distinct rooting phases, potentially aiding in AR formation. Transcriptomic analysis during the early stages of root formation shows significant downregulation of the abscisic acid and jasmonate signaling pathways, while ethylene and cytokinin signaling seems upregulated. Network analysis of genes involved in carbon metabolism and photosynthesis indicates that the basal region of the micro-cuttings undergoes rapid reprogramming, which results in the breakdown of sugars into pyruvate. This pyruvate is then utilized to fuel the tricarboxylic acid cycle, thereby sustaining growth through aerobic respiration. Collectively, our findings provide a time-course morphophysiological dissection and also suggest the regulatory role of a conserved auxin module in AR development in these species.

Influence of natural and non-natural diets on the fitness and rearing of Pectinophora gossypiella Saunders.

In order to develop integrated management approaches for Pectinophora gossypiella, basic studies are crucial. The two-sex life table is the most important tool for describing the fitness and population parameters of both sexes (male and female) of an insect, while the traditional life table only explains the female sex of an insect. However, no study has reported on the biology of P. gossypiella using two-sex life table tools. Therefore, this study explains the rearing dynamics of P. gossypiella on a cotton seed-based artificial diet and a natural diet (mature cotton bolls). According to the results, the oviposition period of P. gossypiella was recorded to be longer on the artificial diet (9.07 ± 0.24) compared to the natural diet (7.40 ± 0.11). The total fecundity of P. gossypiella was greater on the artificial diet (125.94 ± 3.06) in comparison to the natural diet (60.37 ± 1.10). The population parameters, including intrinsic rate of increase, finite rate of increase, gross reproductive rate, and net reproductive rate of P. gossypiella were highest on the artificial diet in comparison to the natural diet. This study concluded that the cotton seed-based artificial diet was most suitable for the rearing of P. gossypiella. In the future, P. gossypiella may be studied in depth in light of the findings in this study.

Identification of Single-Nucleotide Polymorphisms (SNPs) Associated with Heat Tolerance at the Reproductive Stage in Synthetic Hexaploid Wheats Using GWAS.

The projected rise in global ambient temperature by 3-5 °C by the end of this century, along with unpredicted heat waves during critical crop growth stages, can drastically reduce grain yield and will pose a great food security challenge. It is therefore important to identify wheat genetic resources able to withstand high temperatures, discover genes underpinning resilience to higher temperatures, and deploy such genetic resources in wheat breeding to develop heat-tolerant cultivars. In this study, 180 accessions of synthetic hexaploid wheats (SHWs) were evaluated under normal and late wheat growing seasons (to expose them to higher temperatures) at three locations (Islamabad, Bahawalpur, and Tando Jam), and data were collected on 11 morphological and yield-related traits. The diversity panel was genotyped with a 50 K SNP array to conduct genome-wide association studies (GWASs) for heat tolerance in SHW. A known heat-tolerance locus, TaHST1, was profiled to identify different haplotypes of this locus in SHWs and their association with grain yield and related traits in SHWs. There was a 36% decrease in grain yield (GY), a 23% decrease in thousand-grain weight (TKW), and an 18% decrease in grains per spike (GpS) across three locations in the population due to the heat stress conditions. GWASs identified 143 quantitative trait nucleotides (QTNs) distributed over all 21 chromosomes in the SHWs. Out of these, 52 QTNs were associated with morphological and yield-related traits under heat stress, while 15 of them were pleiotropically associated with multiple traits. The heat shock protein (HSP) framework of the wheat genome was then aligned with the QTNs identified in this study. Seventeen QTNs were in proximity to HSPs on chr2B, chr3D, chr5A, chr5B, chr6D, and chr7D. It is likely that QTNs on the D genome and those in proximity to HSPs may carry novel alleles for heat-tolerance genes. The analysis of TaHST1 indicated that 15 haplotypes were present in the SHWs for this locus, while hap1 showed the highest frequency of 25% (33 SHWs). These haplotypes were significantly associated with yield-related traits in the SHWs. New alleles associated with yield-related traits in SHWs could be an excellent reservoir for breeding deployment.

Analysis of Incidence and Reasons for Re-intervention After Aortic Valve Replacement Using the Trifecta Aortic Bioprosthesis.

Trifecta bioprosthesis claims favorable hemodynamic performance. However, reports of structural valve deterioration (SVD) raise concerns of its long-term durability. We assessed outcomes with the Trifecta valve over a 10-year period. All patients receiving Trifecta bioprostheses between October 2011 and October 2020 were included. Perioperative and survival characteristics were prospectively collated in an independent database. Re-intervention was recorded as a surrogate for SVD. Nine hundred and forty-four patients (mean age 72.82 years ± 8.13, 58% male) underwent aortic valve replacement with the Trifecta valve. At 10-years, 1.4% of patients required a redo operation, giving an overall freedom from re-intervention of 98.6%. The mean time to re-intervention was 48.87 months. Survival was 73.58% and 76.92% in patients who did not require re-intervention vs re-intervention group, respectively. In a large, single-center cohort, the Trifecta aortic bioprosthesis had a 1.4% all-cause re-intervention rate at 10-years, with insignificant impact on survival.

A Machine-Learning-Based Robust Classification Method for PV Panel Faults.

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter.

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.

Factors affecting youth empowerment and entrepreneurial initiatives: Social implications and way forward.

This study aims to identify the factors that impact the empowerment of Pakistani youth and their entrepreneurial initiatives in Pakistan. A sample of 350 youngsters from renowned Pakistani universities across the country was selected for this purpose. The questionnaire was administered in person and electronically. Statistical Package of Social Sciences (SPSS) was used for analyzing the data. The findings indicated that all the hypothesized factors (government policies, lack of political participation, employment opportunities, and social engagement) had an impact on youth empowerment and their ability to initiate entrepreneurial activities. This study identified the set of factors that impact empowerment in youngsters, and this model can be extended to other contexts and additional factors can be included. The analytical findings from this study serve to help the government formulate appropriate policies for underserved youth and include them in the policy-making process so that the factors that hinder their empowerment and entrepreneurial initiatives could be addressed.

Genome-wide association for heat tolerance at seedling stage in historical spring wheat cultivars.

Increasing global temperature has adverse effects on crop health and productivity at both seedling and reproductivity stages. It is paramount to develop heat tolerant wheat cultivars able to sustain under high and fluctuating temperature conditions. An experiment was conducted to characterize 194 historical wheat cultivars of Pakistan under high temperature at seedling stage to identify loci associated with heat tolerance using genome-wide association studies (GWAS). A quantitative trait locus, TaHST1, on chr4A was also characterized to identify the haplotypes at this locus associated with heat tolerance in wheat from Pakistan. Initially, the diversity panel was planted under control conditions (25°C/20°C day and night temperature) in a glass house. At three leaf stage, plants were subjected to heat stress (HS) by increasing temperature (40°C/35°C day and night), while one treatment was kept at control condition. After 7 days of HS, data were collected for seedling morphology. Heat stress reduced these traits by 25% (root weight) to 40% (shoot weight), and shoot biomass was largely affected by heat stress. A GWAS model, fixed and random model circulating probability unification (FarmCPU), identified 43 quantitative trait nucleotides (QTNs) on all chromosomes, except chr7B, were associated under both HS and control conditions. Thirteen QTNs were identified in control, while 30 QTNs were identified in HS condition. In total, 24 haplotypes were identified at TaHST1 locus, and most of the heat tolerant genotypes were assigned to Hap-20 and Hap-21. Eleven QTNs were identified within 0.3-3.1 Mb proximity of heat shock protein (HSP). Conclusively, this study provided a detailed genetic framework of heat tolerance in wheat at the seedling stage and identify potential genetic regions associated with heat tolerance which can be used for marker assisted selection (MAS) in breeding for heat stress tolerance.

Role of Extrinsic Cues in the Formation of Quality Perceptions.

Examining the quality perceptions of consumers has often been recommended as an international research paradigm. This study is grounded in the Pakistani consumer market to evaluate the impact of food packaging cues on perceived product quality. The moderating effect of consumer knowledge was also taken into consideration in the study. A signaling theory was used in the study for its established predictive power in consumer behavior, marketing, and various fields of research. Based on the essence of the signaling theory, this study hypothesized that food packaging cues cast a positive impact on perceived product quality and consumer knowledge moderates these relationships. By using the sample of 504 consumers, data were gathered using the mall intercept method following a multi-stage sampling technique. The responses were analyzed using Statistical Package for Social sciences (SPSS) and Smart Partial Least Square (PLS). The findings of this study unveil that the extrinsic cues' brand name, price, nutritional labels, and precautionary labels were positively and significantly related to the perceived product quality. However, the country of origin cast no impact on the perceived product quality. Consumer knowledge reflected a moderation effect on the relationships between brand name and country of origin with the perceived product quality whereas it exerted no moderation impact on the relationships of price, nutritional labels, and precautionary labels with the perceived product quality. As the results exhibit that Pakistani consumers rely on food packaging cues for perceiving a product, hence it is recommended that marketers and policymakers develop appropriate marketing strategies focused on the significance of food packaging cues.

Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system.

Recommender systems (RSs) have become increasingly vital in the modern information era and connected economy. They play a key role in business operations by generating personalized suggestions and minimizing information overload. However, the performance of traditional RSs is limited by data sparseness and cold-start issues. Though deep learning-based recommender systems (DLRSs) are very popular, they underperform when considering rating matrices with sparse entries. Despite their performance improvements, DLRSs also suffer from data sparsity, cold start, serendipity, and generalizability issues. We propose a multistage model that uses multimodal data embedding and deep transfer learning for effective and personalized product recommendations, and is designed to overcome data sparsity and cold-start issues. The proposed model includes two phases. In the first-offline-phase, a deep learning technique is implemented to learn hidden features from a large image dataset (targeting new item cold start), and a multimodal data embedding is used to produce dense user feature and item feature vectors (targeting user cold start). This phase produces three different similarity matrices that are used as inputs for the second-online-phase to generate a list of top-n relevant items for a target user. We analyzed the accuracy and effectiveness of the proposed model against the existing baseline RSs using a Brazilian E-commerce dataset. The results show that our model scored 0.5882 for MAE and 0.4011 for RMSE which is lower than baseline RSs which indicates that the model achieved an improved accuracy and was able to minimize the typical cold start and data sparseness issues during the recommendation process.

In silico analysis and expression profiling of Expansin A4, BURP domain protein RD22-like and E6-like genes associated with fiber quality in cotton.

BACKGROUND: To supply high-quality cotton fibre for the textile industry, the development of long, strong and fine fibre cotton varieties is imperative. An interlinked approach was used to comprehend the role of fibre genes by analyzing interspecific progenies of cotton species. Wild Gossypium species and races are rich source of genetic polymorphism due to environmental dispersal and continuous natural selection. These genetic resources hold mass of outclass genes that can be used in cotton improvement breeding programs to exploit possible traits such as fibre quality, abiotic stress tolerance, and disease and insect resistance. Therefore, use of new molecular techniques such as genomics, transcriptomics and bioinformatics is very important to utilize the genetic potential of wild species in cotton improvement programs. METHODS: Interspecific lines and Gossypium species used in the study were grown at Central Cotton Research Institute (CCRI), Multan. After retrieving DNA sequence of the genes from NCBI, the primers for gene expression and full-length gene sequence were designed. Expression profiling of Expansin A4, BURP Domain protein RD22-like and E6-like fibre genes was performed through Real Time PCR. BLAST and DNA sequence alignment was conducted for sequence comparison of interspecific lines and Gossypium species. Different in silico analysis were used for characterization of fibre genes and identification of cis acting promoter elements in promoter region. RESULTS: Variable expression of genes related to fibre development was observed at different stages. BLAST and DNA sequence alignment demonstrated resemblance of interspecific lines with G. hirsutum. In silico analysis on the sequence data also confirmed the role of Expansin A4, BURP Domain protein RD22-like and E6-like fibre genes in fibre development. Genetic engineering is also recommended by transferring E6-like, Expansin A4 and BURP Domain RD22-like genes in local cotton cultivars. Similarly, several stress tolerant and light responsive cis acting elements were identified through promotor analysis, which may contribute for fibre development in the breeding programs. CONCLUSION: Expansin A4, BURP Domain RD22-like and E6-like have positive role in fibre development with variable expression at fiber length and strength associated stages.

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats.

Stripe rust caused by Puccnina striiformis (Pst) is an economically important disease attacking wheat all over the world. Identifying and deploying new genes for Pst resistance is an economical and long-term strategy for controlling Pst. A genome-wide association study (GWAS) using single nucleotide polymorphisms (SNPs) and functional haplotypes were used to identify loci associated with stripe rust resistance in synthetic-derived (SYN-DER) wheats in four environments. In total, 92 quantitative trait nucleotides (QTNs) distributed over 65 different loci were associated with resistance to Pst at seedling and adult plant stages. Nine additional loci were discovered by the linkage disequilibrium-based haplotype-GWAS approach. The durable rust-resistant gene Lr34/Yr18 provided resistance in all four environments, and against all the five Pst races used in this study. The analysis identified several SYN-DER accessions that carried major genes: either Yr24/Yr26 or Yr32. New loci were also identified on chr2B, chr5B, and chr7D, and 14 QTNs and three haplotypes identified on the D-genome possibly carry new alleles of the known genes contributed by the Ae. tauschii founders. We also evaluated eleven different models for genomic prediction of Pst resistance, and a prediction accuracy up to 0.85 was achieved for an adult plant resistance, however, genomic prediction for seedling resistance remained very low. A meta-analysis based on a large number of existing GWAS would enhance the identification of new genes and loci for stripe rust resistance in wheat. The genetic framework elucidated here for stripe rust resistance in SYN-DER identified the novel loci for resistance to Pst assembled in adapted genetic backgrounds.

Towards Automatic License Plate Detection.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles' license plates in images is a critical step that has a substantial impact on any ALPD system's recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.

Subjective Experiences of Alzheimer's Disease in the Pakistani Cultural Context: An Exploratory Study.

Studying Alzheimer's disease with the fluctuating environmental, individual, and cultural factors in pertinence to the Diagnostic Statistical Manual (DSM-5) requires information, awareness, and understanding of the disease. Studies have calibrated sociocultural factors to be imperative in the expression of functional symptomology of Alzheimer's disease. Pakistan as a sociocentric, predominantly Muslim country, calls for such efforts. The current research was conducted to study the functional symptomology and experiences of Alzheimer's disease. A qualitative approach was adopted in which in-depth interviews of three dyads of patients with Alzheimer's disease and their caregivers (N = 6) were conducted. Interpretative Phenomenological Analysis was applied to acquire the thematic analysis of data. Results showed sociocentricism as a forefront factor. Cognition, behavior, and emotions were found to be functionally expressed by religion, unawareness, respect of older people, stigmatization, and isolation within family dynamics. The study could be an instigator for further culture-oriented assessment and management providing services.

Neutrophils to lymphocytes ratio between benign and malignant thyroid nodule.

OBJECTIVES: To determine the relationship of Neutrophils to lymphocytes ratio (NLR) values with the benign and malignant thyroid nodules. METHODS: In this cross-sectional clinical study conducted from September 1st 2020 to February 28th 2021, we included 216 patients who underwent thyroidectomy at Jinnah Postgraduate Medical Centre Karachi Pakistan. After thyroidectomy specimens were sent for pathologic examination. Patients were divided into two categories based on histopathologic results; Malignant nodule and benign nodules. Data of complete blood count was obtained from the pre-operative lab investigations and NLR was calculated. RESULTS: There were 42 (26%) men, and 116 (74%) women of 158 in the BTN group, 18 (31%) men, and 40 (69%) women of 58 in the MTN group. The mean age of 48 ± 6 years in the BTN group as well as 47 ±8 years in the MTN group (p-value 0.32). The mean neutrophil count in the BTN group was 4.26 ± 2.8 versus 4.41 ± 2.2 (x 1000/mm3)) the malignance thyroid group (p-value = 0.71). The mean lymphocyte count was 3.81 ± 0.9(x 1000/mm3) in the BTN group and 3.61± 1.2 (x 1000/mm3) in the malignance group (p-value = 0.18). The mean NLR value for the benign thyroid nodular group was 1.19 ± 2.2 and 1.22±1.8 in the malignant thyroid nodular group (p-value = 0.92). CONCLUSION: According to the results of this study, we concluded that preoperative period biochemistry laboratory results such as neutrophils count, lymphocyte count, and NLR value don't provide enough evidence to differentiate between benign and malignant thyroid carcinoma.

Monitoring of field-evolved resistance to flonicamid, neonicotinoid, and conventional insecticides in the Oxycarenus hyalinipennis costa.

Oxycarenus hyalinipennis Costa is a polyphagous insect pest and can develop insecticide resistance. The resistance of O. hyalinipennis to neonicotinoids (clothianidin and dinotefuran), flonicamid, and conventional insecticides; carbamates (methomyl and carbosulfon), organophosphates (chlorpyrifos and malathion), and pyrethroids (cypermethrin and zeta-cypermethrin) was evaluated. The O. hyalinipennis populations were sampled from four locations in Pakistan and performed bioassays against the insecticides by leaf dip protocol. The O. hyalinipennis' populations showed low resistance to carbosulfan (resistance ratio (RR) = 2.06-6.34) and methomyl (RR = 2.78-7.27), moderate to high resistance to chlorpyrifos (RR = 30-45), malathion (RR = 20.29-88.19), and flonicamid (RR = 14.24-46.97), in comparison with the susceptible strain. Susceptibility to low resistance against cypermethrin (RR = 1.27-2.82), zeta-cypermethrin (RR = 2.62-3.38), and clothianidin (RR = 1.74-3.40), and low to moderate resistance to dinotefuran (RR = 3.84-13.43) in the field populations, was observed compared to the susceptible strain. A rotational usage of carbamates and pyrethroids with an integrated pest management tool should be considered to deal with O. hyalinipennis' insecticide resistance.

Psychosocial Reactions of Pakistani Students Towards COVID-19: A Prevalence Study.

BACKGROUND: The current study aimed at investigating the manifestation and prevalence of the commonly reported psychosocial reactions in the university students following the onset of COVID-19 pandemic. METHODS: This cross-sectional survey was carried out during April to May, 2020 in the city of Lahore. Based on the interviews and presenting problems of thirty-nine self-referred students to student counselling service center, a list of twenty-seven self-report measure was presented through an online cross-sectional survey of 510 students enrolled in a private institute. The age range of the participants was between 17-26 years (M, 21.86; SD, 2.94). RESULTS: The results showed that the most frequently reported reactions by university students during COVID-19 is restricted daily routine (92%), preoccupation with cleanliness (86%), feeling uncertain about future (85%), feeling bored (84%) and low mood (84%). The least frequently reported reaction was financial crisis (48%). The findings further showed that 18% of the participants had mild, 34% had moderate, 29% severe and 19% very severe level of problems. CONCLUSIONS: The finding revealed that university students have significantly affected by this pandemic which need attention from mental health professionals.

Wheat Yellow Rust Disease Infection Type Classification Using Texture Features.

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20-30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.