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Ebinat Woreda Livestock Resource Development and Promotion Office

Abebe Mequanent

Manual For Fish Production Constraints And Fish Diseases in Ethiopia, Ebinat, Ethiopia

E-mail: abebemequanent@gmail.com

ABSTRACT: Fish is a limbless cold blooded vertebrae animal with gills and fins, living wholly in water. Fish farming is practiced in different parts of the World including Ethiopia. It is concentrated in Lake Tana and Rift valley lakes of the country. Even though Ethiopia has a beautiful water bodies with total surface area of 13,637 km² that have a potential to produce 94,541 tons annually, but the country only produce 45,610 tons of fish per year from both capture and Aquaculture fisheries, because of many constraints like: post harvest loss, poor infrastructure, less access to fishing materials, lack of market chain, over fishing, urbanization, agricultural expansion, wet land degradation, water hyacinth, climate change and fish disease, just like all other animals fish also suffer from various diseases and effects of pollutants. Fish disease such as:- viral, bacterial, fungal and parasitic fish diseases have a significant effect on fish production.

[Abebe M. Manual for Fish production constraints and Fish diseases in ethiopia. Life Sci J 2024;21(5):1-15]. ISSN 1097-8135 (print); ISSN 2372-613X (online). http://www.lifesciencesite.com. 01. doi:10.7537/marslsj210524.01.

Key words: Constraints, diseases, Ethiopia, fish and production

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Using Questionnaire of EQ-5D to Assess the Impact of Line Dancing Exercise on Older Adults with Dysmobility Syndrome

*Xiaoli Liao

Henan Huaxia Esti Biotech Co., Ltd.

Room 1308, Building 5, R&D, No. 11 Changchun Road, Zhengzhou High tech Zone

*First and Corresponding author E-mail：liao1037392291@126.com

ABSTRACT: Geriatric dysmobility syndrome (DMS) is a public health concern for the elderly. However, a comprehensive self-rated assessment of exercise in the elderly with DMS has rarely been validated. Using a simple effective self-rated dichotomous EQ-5D questionnaire, we aimed to assess the impact of line dancing exercise on older adults with DMS. Via line dancing training exercise, we did survey to investigate the customer value on loyalty included 199 older adults living in an elderly community with a mean age of 68±2 years, of whom 76 diagnosed with DMS. According to their wishes, we arranged the participants either into line dancing group (IG) or control group (CG) via the estimation of self-rated EQ-5D questionnaire, Cox and Logistic regression models to assess the impact of line dancing exercise on older adults with DMS. We calculated odds ratio, predicted probability, hazard ratio (HR), and recovery rate (RR) in comparison between the IG and CG. In crosstabulation analysis, the hazard ratio for DMS in IG compared to CG was (HR, 0.836, 95% C.I., 0.687-1.018, p<0.005). Within 24 weeks, the hazard ratio changed significantly (HR, 5.365, 95% C.I., 3.478-8.275, p<0.005). Through a binary logistic regression model, DMS was considered as the outcome and self-rated dichotomous EQ-5D total score as a predictor, at baseline, the odds ratio was (OR, 1.269, 95% C.I., 0.707-2.278, P>0.05). After 24 weeks of follow-up, in IG, exercise impact described by (OR, 0.05, 95% C.I., 0.6-0.079, P<0.001). The Cox regression model predicted that the elderly with an EQ-5D total score of 7 had a 99% rehabilitation from DMS after 18 weeks and a 45% in 6 weeks. Relapse rate was low to 0.1% in these short-term 24 weeks study and neglected. This study revealed the impact of the line dancing exercise on the older adults with dysmobility syndrome in 24 weeks via the simple effective self-rated EQ-5D analysis as well as Cox and Logistic regression models.

[Xiaoli Liao. Using Questionnaire of EQ-5D to Assess the Impact of Line Dancing Exercise on Older Adults with Dysmobility Syndrome. Life Sci J 2024;21(5):16-25]. ISSN 1097-8135 (print); ISSN 2372-613X (online). http://www.lifesciencesite.com. 02. doi:10.7537/marslsj210524.02.

Keywords: Dysmobility syndrome, Line dancing, Logistic regression model

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Bisphenol A: The Ubiquitous Chemical in Our Daily Lives

Sanman Samova¹* and Hetal Doctor²

^*1Gujarat Biotechnology Research Centre, Gandhinagar, Gujarat, India

²President Science College, Ahmedabad, Gujarat, India

*Corresponding author: E-mail: samova.sanman@gmail.com

Abstract: Bisphenol A (BPA) is a synthetic organic compound widely used in the production of polycarbonate plastics and epoxy resins. Its prevalence in everyday products such as water bottles, food containers, and thermal paper has raised significant health concerns due to its potential endocrine-disrupting properties. BPA can mimic estrogen and bind to estrogen receptors, leading to disruptions in hormonal signaling pathways. This interference can affect reproductive health, particularly in young individuals, by causing irregularities in menstrual cycles, ovulation, and spermatogenesis. Studies have shown that BPA exposure can impair ovarian function, reduce sperm quality, and induce oxidative stress and inflammation, further impacting fertility. The potential for BPA to cause long-term health effects through epigenetic modifications and its presence in food contact materials emphasize the need for strategies to minimize exposure. These include using BPA-free products, opting for fresh foods over canned goods, and advocating for stricter regulations. Understanding the mechanisms underlying BPA's reproductive toxicity is crucial for developing interventions to mitigate its impact. Further research is needed to explore the cumulative effects of low-dose exposure, the transgenerational impacts, and the specific effects on male fertility. Addressing these research gaps is essential for protecting reproductive health and informing public health policies.

[Sanman Samova and Hetal Doctor, Bisphenol A: The Ubiquitous Chemical in Our Daily Lives. Life Sci J 2024;21(5):26-32]. ISSN 1097-8135 (print); ISSN 2372-613X (online). http://www.lifesciencesite.com. 03. doi:10.7537/marslsj210524.03.

Keywords: Bisphenol A; Ubiquitous; Chemical; Daily Lives

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DRY PEAS-2018 variety: AN INNOVATIVE HIGH YIELDING AND POWDERY MILDEW RESISTANT DRY PEA, RICH SOURCE OF PROTEIN

Ch. Muhammad Rafiq¹_, Muhammad Amin¹_,  Amer Hussain^1, Amir Amin, Sadia Kaukab and Muhammad Shafiq¹

1-Pulses institute  Ayb Agricultral Research Institute Faisalabd  Pakistan.

Corresponding author: Muhammad Amin

Email: aminpbg@gmail.com

ABSTRACT: Dry pea-2018 is a bold seeded, powdery mildew, pea rust resistant and high yielding variety of dry peas (Pisum sativum L.). it is a cross between two genotypes (T-29 × T-37). The hybrid/cross work was started at area of Pulses Institute AARI Faislabad Pakistan in year 2002-03. It has used pedigree method of selection. The segregating progenies was evaluated and advanced to F5 generation after fulfilling suitable standards. The pure line was routed from F5 source in during 2007-08 and completed evaluate in yield nursery during 2008-09 for further judgment.  It was evaluated for two years in station yield trials from 2009-10 to 2010-11 to meet its objectives before moving further to micro yield trials in 2011-12 to 2012-13.  It was evaluated in pathological, entomological studies, adaptation yield trials and micro yield trials in the same year for different parameters to complete. The variety dry peas-2018 produced more seed yield (10-56 %) in different yield trials over respective checks with a yield power of 3750 kg hectare^-1. It come out first in 2011-12 and 2013-14 in micro and multi locational yield trials. This is bold seeded variety which suitable for commercial purpose. It has 25.3 % protein contents. Its flower size is large, white in color. It takes 75-80 days to flower (50 %) after sowing. Pod size larger than check variety Number 267 having 6.1-6.5 cm in length. The number of pod range 26-65 with 3-4 seeds pod^-1 while no pod shattering. grain color is beige shiny, large shape.  Seed size is larger than check Number 267 having 170 gram 1000-seed weight. Its plant shape is semi erect with range of 80-160 cm in plant height; taller than check variety Number 267. The plant stem is light green in color. Number of primary branches ranged from 2-6 .it has secondary branches ranged from 7-10. Leaf color is light green, leaf size is large, pubescence is absent and large size leaflets. It indicates good results at seed rate of 60 kg hectare^-1 with 15 cm plant to plant and 24-60-0 kg NPK hectare^-1. Resistance against powdery mildew, pea rust, bold seeded good yield potential and good are major attributes of new dry pea variety “Dry peas-2018”.

[Ch. Muhammad Rafiq_, Muhammad Amin_, Amer Hussain^, Amir Amin, Sadia Kaukab and Muhammad Shafiq, DRY PEAS-2018 variety: AN INNOVATIVE HIGH YIELDING AND POWDERY MILDEW RESISTANT DRY PEA, RICH SOURCE OF PROTEIN. Life Sci J 2024;21(5):33-38]. ISSN 1097-8135 (print); ISSN 2372-613X (online). http://www.lifesciencesite.com. 04. doi:10.7537/marslsj210524.04.

KEY-WORD: Pisum sativum; genotype; cross-breeding;  hybrid;  more  yielder variety; disease resistant; protein good; Pakistan; quality

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Enhancing Arabic Reading Proficiency through  Artificial Intelligent Application

Abstract: According to research, learning to read in classical Arabic is more difficult and time-consuming than learning to read in other languages. In addition to the complexity of the language's grammar and syntax, the difficulty of reading Arabic is further compounded by the fact that it is a non-Latin alphabet. This means that the language is not based on the same phonetic principles as English or other European languages. In fact, many people fall into mistakes when reading classical Arabic, such as misreading Harkat. This research proposes a solution to correct reading mistakes in classical Arabic by using a machine learning and natural language processing approach. In this research, the sound of the Arabic reader was recorded and converted into Arabic text to recognize the characters of the text using the Google API. The dataset was collected utilizing Google Forms and Arabic audio websites and was then labeled manually. The natural language processing approach was employed to detect prevalent patterns in misreadings of classical Arabic texts. Subsequently, a collection of machine learning algorithms was trained to recognize and correct the errors committed by readers. The effectiveness of these algorithms was then evaluated through severe testing, revealing that the random forest algorithm attained the highest level of accuracy, with an impressive accuracy rate of 81%. The outcomes of this investigation provide compelling evidence that the proposed machine learning approach holds immense potential for significantly enhancing the accuracy of reading classical Arabic texts.

[Ashwag Maghraby_, Azzah Mahjoob_,  Waad Alsaedi,Atheer Alsaedi，Manar Alsaedi，Amirah Alotaibi. Enhancing Arabic Reading Proficiency through  Artificial Intelligent Application. Life Sci J 2024;21(5):39-49]. ISSN 1097-8135 (print); ISSN 2372-613X (online). http://www.lifesciencesite.com. 05. doi:10.7537/marslsj210524.05.

Keywords: Machine learning (ML), Natural language processing (NLP), Classical Arabic error detection, Self-learning app

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