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- 검색키워드
- 저자 : Muhammad Ali Akhtar (검색결과 22 건)
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Akhtar Muhammad Uzair,Hifzulrahman,Pasha Talat Naseer,Avais Muhammad,Khan Nauman,Chishti Ghazanfar Ali,Ali Mubashar,Imran Muhammad,Tahir Muhammad Naeem,Naveed-ul-Haque Muhammad 아세아·태평양축산학회 2023 Animal Bioscience Vol.36 No.6
Objective: Hyperketonemia remains a major metabolic issue of serious milk production and a major health concern in early lactation cows. Oral supplementation of glucose precursors (GP) can be used to prevent hyperketonemia in dairy cows. The objective of this study was to compare the beneficial effects of orally supplementing a mixture of GP on metabolic health indicators and milk production status of primiparous (PP) and multiparous (MP) dairy cows. Methods: Twenty-eight Holstein cows were blocked by expected date of parturition, previous lactation yield, and parity. The cows were randomly allocated to one of the four treatment groups (n = 7 cows/group) based on their parity and GP supplementation: i) PP cows fed basal diet only (PP-CON), ii) PP cows with oral supplementation of GP (PP-GP), iii) MP cows fed basal diet only (MP-CON), and iv) MP cows with oral supplementation of GP (MP-GP). Glucose precursor (glycoline liquid) was orally drenched (300 mL/d) in GP cows from 7 days prepartum through 7 days postpartum. Other than GP supplementation, all cows were fed similar pre- and postpartum basal diets. Results: In both pre- and postpartum periods, serum glucose concentration was increased, whereas β-hydroxybutyrate and free fatty acids were decreased in GP cows compared with the CON cows. Milk yield and milk components were statistically not different between GP and CON cows over the first 9 week of lactation. The yield of actual milk, energycorrected milk, 63-days cumulative milk, colostrum yield, and calf birth weight remained higher in MP cows compared with PP cows. Conclusion: Oral drenching of GP around calving can be recommended to successfully improve the metabolic health and reduce the negative effects of hyperketonemia not only in MP but also in PP dairy cows.
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An Application of Machine Learning in Retail for Demand Forecasting
Muhammad Umer Farooq,Mustafa Latif,Waseem,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8
Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.
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An Application of Machine Learning in Retail for Demand Forecasting
Muhammad Umer Farooq,Mustafa Latif,Waseemullah,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9
Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.
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Ali, Kashif,Mahjabeen, Ishrat,Sabir, Maimoona,Baig, Ruqia Mehmood,Zafeer, Maryam,Faheem, Muhammad,Kayani, Mahmood Akhtar Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.18
Apurinic/apyrimidinic endonuclease 1 (APEX1) is a multifunctional protein which plays a central role in the BER pathway. APEX1 gene being highly polymorphic in cancer patients and has been indicated to have a contributive role in Apurinic/apyrimidinic (AP) site accumulation in DNA and consequently an increased risk of cancer development. In this case-control study, all exons of the APEX1 gene and its exon/intron boundaries were amplified in 530 breast cancer patients and 395 matched healthy controls and then analyzed by single-stranded conformational polymorphism followed by sequencing. Sequence analysis revealed fourteen heterozygous mutations, seven 5'UTR, one 3'UTR, two intronic and four missense. Among identified mutations one 5'UTR (rs41561214), one 3'UTR (rs17112002) and one missense mutation (Ser129Arg, Mahjabeen et al., 2013) had already been reported while the remaining eleven mutations. Six novel mutations (g.20923366T>G, g.20923435G>A, g.20923462G>A, g.20923516G>A, 20923539G>A, g.20923529C>T) were observed in 5'UTR region, two (g.20923585T>G, g.20923589T>G) in intron1 and three missense (Glu101Lys, Ala121Pro, Ser123Trp) in exon 4. Frequencues of 5'UTR mutations; g.20923366T>G, g.20923435G>A and 3'UTR (rs17112002) were calculated as 0.13, 0.1 and 0.1 respectively. Whereas, the frequency of missense mutations Glu101Lys, Ser123Trp and Ser129Arg was calculated as 0.05. A significant association was observed between APEX1 mutations and increased breast cancer by ~9 fold (OR=8.68, 95%CI=2.64 to 28.5) with g.20923435G>A (5'UTR), ~13 fold (OR= 12.6, 95%CI=3.01 to 53.0) with g.20923539G>A (5'UTR) and~5 fold increase with three missense mutations [Glu101Lys (OR=4.82, 95%CI=1.97 to 11.80), Ser123Trp (OR=4.62, 95%CI=1.7 to 12.19), Ser129Arg (OR=4.86, 95%CI=1.43 to 16.53)]. The incidence of observed mutations was found higher in patients with family history and with early menopause. In conclusion, our study demonstrates a significant association between germ line APEX1 mutations and breast cancer patients in the Pakistani population.
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Metathesis of 2-pentene over Mo and W supported mesoporous molecular sieves MCM-41 and SBA-15
Mohamed Ali Ibrahim,Muhammad Naseem Akhtar,Ji rí Cejka,Erica Montanari,Hynek Balcar,Martin Kubu,Sulaiman S. Al-Khattaf 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.53 No.-
Molybdenum and tungsten oxides were supported on silica, MCM-22, MCM-41 and SBA-15. XRD and N2 adsorption–desorption revealed that architecture and textural character of supports were preserved. The catalysts were investigated in transformation of 2-pentene at different reaction temperatures. MoO3/ MCM-22 exhibited highest conversions with isomerization and cracking as major reactions. MoO2(acac)2, MoO3 and WO3 supported on MCM-41 and SBA-15 showed metathesis reaction of 2-C5 = producing propylene, C4 = and C6+ =as major products. Catalysts based on MCM-41 exhibited higher activity and stability than SBA-15. Addition of ethylene to 2-C5 = increased selectivity to propylene due to metathesis of ethylene with 2-pentene.
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Naheed Akhtar,Vinitha Moolchand Thadhani,Faraz Ul Haq,Muhammad Noman Khan,Sajjad Ali,Syed Ghulam Musharraf 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.90 No.-
Tea beverages have been enjoyed globally for the last several decades. The present study focuses on the comprehensive chemical and biological analysis of three processed tea products. A total of sixty-three compounds in processed samples were identified based on their exact masses and fragmentation patterns using LC-ESI-QTOF-MS/MS. Furthermore, quantification of eight analytes including caffeine (1), theophylline (2), (+)-catechin (3), (-)-epicatechin (4), (-)-epicatechin gallate (5), (-)-gallocatechin (6), (-)-epigallocatechin gallate (7) and quercetin-3-d-β-glucoside (8) in tea samples was performed using LC-ESI-IT-MS/MS. The concentrations of analytes were found in the range of 0.03 mg/g to 75.58 mg/g in all tea samples. The developed method showed excellent accuracy as %bias ranged from 0.2–3.69% and a good precision with %RSD ranged from 0.03 to 5.11. The LOD and LOQ for all the analytes were found to be in the range of 0.16–3.26 ng/mL and 0.44–9.87 ng/mL, respectively. The DPPH scavenging effects of samples were also investigated and all the samples were found to be strong scavengers of DPPH radical showing 69.50 ± 0.01–78.60 ± 0.10 %RSA. The established method provides a useful way for understanding the metabolite distribution in processed products of C. sinensis and to develop quality control protocols for these products.
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Iftikhar Ali Khan,Khalid Pervaiz Akhtar,Fazal Akbar,Ishtiaq Hassan,Imran Amin,Muhammad Saeed,Shahid Mansoor 한국식물병리학회 2016 Plant Pathology Journal Vol.32 No.1
Cotton leaf curl is devastating disease of cotton characterized by leaf curling, vein darkening and enations. The disease symptoms are induced by DNA satellite known as Cotton leaf curl Multan betasatellite (CLCuMuB), dominant betasatellite in cotton but another betasatellite known as Chili leaf curl betasatellite (ChLCB) is also found associated with the disease. Grafting experiment was performed to determine if host plant resistance is determinant of dominant population of betasatellite in cotton (several distinct strains of CLCuMuB are associated with the disease). Infected scion of Gossypium hirsutum collected from field (the source) was grafted on G. arboreum, a diploid cotton species, resistant to the disease. A healthy scion of G. hirsutum (sink) was grafted at the top of G. arboreum to determine the movement of virus/betasatellite to upper susceptible scion of G. hirsutum. Symptoms of disease appeared in the upper scion and presence of virus/betasatellite in the upper scion was confirmed via molecular techniques, showing that virus/betasatellite was able to move to upper scion through resistant G. arboreum. However, no symptoms appeared on G. arboreum. Betasatelites were cloned and sequenced from lower scion, upper scion and G. arboreum which show that the lower scion contained both CLCuMuB and ChLCB, however only ChLCB was found in G. arboreum. The upper scion contained CLCuMuB with a deletion of 78 nucleotides (nt) in the non-coding region between Arich sequence and βC1 gene and insertion of 27 nt in the middle of βC1 ORF. This study may help in investigating molecular basis of resistance in G. arboreum.
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Khalil Akhtar,Syed Wadood Ali Shah,Assar Ali Shah,Muhammad Shoaib,Syed Kashif Haleem,Nighat Sultana 한국응용생명화학회 2017 Applied Biological Chemistry (Appl Biol Chem) Vol.60 No.4
The aim of present study was to evaluate the antihyperglycemic and antihyperlipidemic effects of aerial parts of Rubus ulmifolius Schott on streptozotocin (STZ)- induced diabetic albino mice. A total of 48-, 60-day-old either sex (male and female) albino mice were treated with, normal control; 2% Tween-80 suspension (diabetic control); glibenclamide (500 lg/kg/orally); RU methanol extract (150 mg/kg/orally) (RUCrd1); RU methanol extract (300 mg/kg/orally) (RUCrd2); RU chloroform extract (150 mg/kg/orally) (RUC); RU ethyl acetate extract (150 mg/kg/orally) (RUE); and RU butanol extract (150 mg/kg/orally) (RUB) for a period of 15 days. Diabetes was induced in albino mice by single intraperitoneal injection of streptozotocin (50 mg/kg/b/w). After 15 days, group treated with glibenclamide, RUCrd1, RUCrd2, RUC, RUE and RUB exhibited a significant (P[0.05) decrease in blood glucose level as compared to diabetic control groups. The total cholesterol, triglycerides and low-density lipoproteins as well as serum creatinine level, serum glutamate pyruvate transaminase, serum glutamate oxaloacetate transaminase and alkaline phosphatase were also significantly (P[0.05) decreased in glibenclamide, RUCrd1, RUCrd2, RUC, RUE and RUB groups albino mice as compared to diabetic control. It was concluded that Rubus ulmifolius Schott extract has positive effect as hypoglycemic and antihyperlipidemic on diabetic albino mice.
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