Assessing the Lack Awareness and Dangers of Dengue in Rural Punjab Pakistan

Dengue is one of the dangerous fevers which are taking hundreds of precious lives every year in Punjab province of Pakistan. This disease is caused by an infection from a virus namely Flavi virus which is transmitted by the mosquito Aedes aegypti, also called Aedes albopictus. This study was undertaken to assess the lack of awareness about the dengue and how dangerous it is in the rural Punjab in order to suggest ways and means to control thy menace. The study has identified that lack of awareness among the rural citizens is the major reason besides integrated policies by the government. 5-point Likert scale was used to gather the opinion from respondents. Cronbach’s alpha was calculated for reliability analysis. Descriptive and inferential analyses were done to find the fact. ?2 test was used to test the hypotheses on ordinal scale for environmental, personal and chemical control as major variables of the study. The study have identified that illiterate were less aware about the environmental and personal control then the literate with no information about chemical control. The study concludes that government must initiate strategies ahead of dengue season to educate the people through massive awareness campaign to save the precious lives. Keywords: Dengue, Environmental, Personal, and Chemical Control

Seed Treatment of Capsicum annuum with Two Different Fungicides to Evaluate the Seed Germination Rate

Chili (Capsicum annuum L.) is the fruit of plants, the Chili is susceptible for several diseases and seed borne fungi Phytophthora capsici which produces collar rot and root of chili. Seed borne pathogens are associated with untreated seeds of chili which are also significantly reduced the germination of seed. This experiment was conducted to find out the seed borne fungi and enhanced the germination of chili (Capsicum annuum) with two fungicides known as Mancozeb 80% WP and Carbendazim 50% WP. Effectiveness of these two fungicides were measured when the seeds planted on blotter paper in petri plates at 270C under lab conditions. These two fungicides significantly reduce the effect of seed borne fungi associated with chili seeds. Mancozeb 80% WP was found most effective to reduce the effect of seed borne fungi and increase the seed germination. Considering the results of the experiment, Mancozeb 80% WP was noted to be a best fungicide against the seed borne fungi. Keywords: Capsicum annuum, Mancozeb, Carbendazim, seed borne fungi, blotter paper. DOI: 10.7176/JBAH/10-4-04 Publication date: February 29th 2020

A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content

An ever-increasing amount of social media content requires advanced AI-based computer programs capable of extracting useful information. Specifically, the extraction of health-related content from social media is useful for the development of diverse types of applications including disease spread, mortality rate prediction, and finding the impact of diverse types of drugs on diverse types of diseases. Language models are competent in extracting the syntactic and semantics of text. However, they face a hard time extracting similar patterns from social media texts. The primary reason for this shortfall lies in the non-standardized writing style commonly employed by social media users. Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns. The key goal is achieved through the incorporation of random weighted perturbation and contrastive learning strategies. On top of a unique training strategy, a meta predictor is proposed that reaps the benefits of 5 different language models for discriminating posts of social media text into non-health and health-related classes. Comprehensive experimentation across 3 public benchmark datasets reveals that the proposed training strategy improves the performance of the language models up to 3.87%, in terms of F1-score, as compared to their performance with traditional training. Furthermore, the proposed meta predictor outperforms existing health mention classification predictors across all 3 benchmark datasets

An Analysis of the Ttitude of People towards Prevention and Control of Dengue in District Bhakkar Punjab Pakistan

Dengue fever took hundreds of precious lives in the last two years in Pakistan in general and Punjab province in particular, which is caused by infection from a virus namely Flavi virus and this disease is transmitted by the mosquito Aedes aegypti, rarely the Aedes albopictus. This study was conducted to analyze the attitude of people towards prevention and control of Dengue in District Bhakkar of Punjab Province of Pakistan. The study has found that poor awareness of the people and ignorance is the major reason besides government failure to educate people and to take preventive measures ahead of the breeding of mosquito Aedes aegypti, the Aedes albopictus especially after rainy season during summer. A structured questionnaire was used to collect primary data, which was feeded to SPSS 16 for data analysis. Cronbach’s alpha was calculated for reliability analysis. Descriptive and inferential analyses were done to find the fact. ?2 test was used to test the hypotheses on ordinal scale for environmental, personal and chemical control as major variables of the study. There was no difference of attitude between literate and illiterate people towards environmental control. The study further identified that literate were more aware about the environmental and personal control then the illiterate, but both having no information regarding chemical control, however, people living in cities were more aware about the environmental and personal control yet, they know how to use chemicals for control of Dengue. The study concludes that government must take measures ahead of dengue season by educating the people through intensive awareness campaign to control environmental contamination besides use of chemicals to prevent the breeding of dengue as an epidemic to save the precious lives. Keywords: Dengue Fever, Environmental Control, Personal Control, and Chemical Control

Single-step synthesis of magnesium-iron borates composite; an efficient electrocatalyst for dopamine detection

© 2020 We have synthesized composite of magnesium-iron borates Mg2(X).Fe3(Y) (here; B2O5 = X and BO6 = Y), using a facile and single-step hydrothermal approach. Where, the synthesis of the composites can help not only to effectively utilize the properties of both i.e. magnesium borates (Mg2B2O5) and iron borates (Fe3BO6) in a single material but also making it thermodynamically more stable at higher temperatures as compared to the individual metallic borates. The as prepared heterostructured-composite was analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray powder diffraction (XRD), Fourier transform infrared (FT-IR), and thermal gravity analysis (TGA) to confirm the morphology, crystallinity, composition, and thermal stability. Further, the composite was explored as a catalyst in the construction of a biosensor where it showed an excellent electrocatalytic activity toward dopamine (DA) oxidation. This study offers a clue to design a borates-based composite to be explored as an efficient electrocatalyst in the application of biosensing field

A Unified Integration Model and Database Management System for Genomic Data

Genomic databases are heterogeneous in nature. This means, they have different notations, formats and terminologies for the same kind of data. To overcome the problems due to the heterogeneous and disperse nature of these databases, two main data integration approaches are used i.e. Mediator-based approach and Data warehouse approach. Systems developed using these approaches allow biologists read-only access of data. At times, they need to submit their own generated data during experiments to databases which is not possible with existing systems. In this paper, we design and develop data warehouse based system which not only allows biologists and researchers to access data but also to submit data they generated during lab experiment

COSMO-RS predictions, hydrogen bond basicity values and experimental evaluation of amino acid-based ionic liquids for lignocellulosic biomass dissolution

© 2018 Elsevier B.V. In this study, the bamboo dissolution capability of six amino acid-based ionic liquids (AAILs) with two different cations, i.e. 1-ethyl-3-methylimidazolium (Emim) and Tetrabutylphosphonium (P4444) and three anions derived from amino acids were investigated by Conductor-like screening model for real solvents (COSMO-RS) prediction and hydrogen bond basicity (β) of Kamlet–Taft parameters. COSMO-RS was used for calculating sigma profile, activity coefficients (γi) and aqueous base dissociation constant of corresponding acids of the anions (pKa) of AAILs. The trends in sigma profile, γi and pKa for AAILs were compared with β values and the effect of structure moiety of ionic liquids was also discussed. The trend of COSMO-RS prediction for anions was noted similar to the β values of AAILs with the exception of serinate anion. Similarly, the trend predicted by COSMO-RS and β values was also found same while changing the cations of AAILs. To investigate, the correlation of the above-mentioned properties with experimental dissolution ability, tetrabutylphosphonium aminoethanic acid ([P4444]Gly) and 1-ethyl-3-methylimidazolium aminoethanic acid ([Emim]Gly) ionic liquids were synthesized and evaluated. The trend predicted by COSMO-RS and the β values were not correlating with efficiency of AAILs for bamboo dissolution. Both AAILs were able to dissolve the bamboo. However, material of [P4444]Gly treated sample was evaluated through XRD analysis where change in crystallinity of cellulose was identified after dissolution and regeneration of bamboo. Scanning Electron microscopy also showed homogenous structure for regenerated materials

Comparison of solvent evaporation and ultrasonicassisted production methods in the development of nimesulide nanosponges and their characterization

Purpose: To compare solvent evaporation and ultrasonic assisted synthesis in preparation of nimesulide nanosponges using polyvinyl-alcohol and Eudragit L100 as a polymer/copolymer and dichloromethane as a cross linker.Methods: Twelve formulations of nimesulide were prepared, six with each method by varying the ratios of both polymer and co-polymer. The resulting nanosponges were evaluated characterized by preformulation studies, production yield (%), differential scanning calorimeter, x-ray diffraction, Fourier transformation infrared spectroscopy, scanning electron microscopy, entrapment efficiency (%), actual drug content (%) and in-vitro dissolution studies.Results: The results revealed that the formulation with high amounts of co-polymer in both methods showed crystalline structures with enhanced dissolution rates in basic media. Drug entrapment was higher for products prepared by solvent evaporation method (74 %) than that prepared by ultrasonic assisted method (61 %). This correlates with the enhanced dissolution rates for products by solvent evaporation method and increased solubility due to drug-polymer complex formation.Conclusion: Formulations made by solvent evaporation method demonstrate higher production yield and drug entrapment. However, both methods exhibit enhanced dissolution rates in basic medium generally as well as other characteristics that are comparable to nanosponges reported in the literature with regard to their comb like structure

Impact of Cover Crop Monocultures and Mixtures on Organic Carbon Contents of Soil Aggregates

Cover crops are considered an integral component of agroecosystems because of their positive impacts on biotic and abiotic indicators of soil health. At present, we know little about the impact of cover crop types and diversity on the organic carbon (OC) contents of different soil aggregate-size classes. In this study, we investigated the effect of cover plant diversity on OC contents of different soil aggregates, such as macro- (\u3c2000–500 µm), meso- (\u3c500–250 µm), and micro-aggregates (\u3c250 µm). Our experiment included a total of 12 experimental treatments in triplicate; six different monoculture treatments such as chickling vetch (Vicia villosa), crimson clover (Trifolium incarnatum), hairy vetch (Vicia villosa), field peas (Pisum sativum), oilseed radish (Raphanus sativus), and mighty mustard (Brassica juncea), and their three- and six-species mixture treatments, including one unplanted control treatment. We performed this experiment usingdeep pots that contained soil collected from a corn-soybean rotation field. At vegetative maturity of cover plants (about 70 days), we took soil samples, and the soil aggregate-size classes were separated by the dry sieving. We hypothesized that cover crop type and diversity will improve OC contents of different soil aggregate-size classes. We found that cover plant species richness weakly positively increased OC contents of soil macro-aggregates (p = 0.056), whereas other aggregate-size classes did not respond to cover crop diversity gradient. Similarly, the OC contents of macroaggregates varied significantly (p = 0.013) under cover crop treatments, though neither monoculture nor mixture treatments showed significantly higher OC contents than the control treatment in this short-term experiment. Interestingly, the inclusion of hairy vetch and oilseed radish increased and decreased the OC contents of macro- and micro-aggregates, respectively. Moreover, we found a positive correlation between shoot biomass and OC contents of macroaggregates. Overall, our results suggest that species-rich rather than -poor communities may improve OC contents of soil macroaggregates, which constitute a major portion of soil systems, and are also considered as important indicators of soil functions

An Agent-based CBIR System for Medical Images

The growing number of image acquisition and storage systems in the digital world demand for the new retrieval methods. Most of the existing retrieval methods use textual information, which has been mainly entered manually for every image in the image collection. In order to access the images of interest, user gives textual input against which images are retrieved from the image collection. Sometimes, this results in garbage retrieval due the human involvement in the image annotation process. So more efficient image retrieval mechanism is needed. To overcome the issue, other approach which is generally considered is content-based image retrieval (CBIR). CBIR depends on the automatically extracted features for every image in the image collection as well as their storage and comparison upon a query. Therefore, feature extraction technique and their storage space are important aspects of CBIR. In this paper, we design and develop agent-based CBIR system for image retrieval and suggest the best feature extraction technique in terms of less storage space and more accurate search results. Although the proposed image retrieval technique can be used for any type image collection, our work focuses on the medical images