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Peste des Petits Ruminants Vaccine (Nigerian Strain 75/1) Confers Protection for at Least 3 Years in Sheep and Goats

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Journal of Biosciences and Medicines 2014年第6期2卷 27-33页

作者： Aamer Bin Zahur Hamid Irshad Aman Ullah Muhammad Afzal asma latif Riasat Wasee Ullah Umer Farooq Muhammad Humayoon Samo Muhammad Jahangir Animal Health Research Laboratories Animal Sciences Institute National Agricultural Research Centre Islamabad Pakistan FAO-UN Pakistan (GCP/PAK/127/USA) NARC Park Road Islamabad Pakistan

The present study reports the duration of immunity and protective efficacy of Peste des Petits Ruminants (PPR) vaccine (Nigerian strain 75/1) in sheep and goats. A total of 105 sheep and goats were divided into three groups A, B and C. Group A received normal recommended dose (1.0 ml) of PPR vaccine, group B received half dose (0.5 ml) of PPR vaccine and group C was kept as unvaccinated control group in contact with vaccinated animals. The post vaccination dynamics of antibodies against PPR virus was studied. It was found that significant antibody titres persisted for 3 years post vaccination in sheep and goats vaccinated with either full dose or half dose of PPR vaccine. The challenge protection studies were carried out in experimental animals at 24 and 36 month post vaccination. The vaccinates withstood challenge and were found completely resistant clinically and virologically to virulent PPR virus for 24 and 36 months post vaccination. The unvaccinated control animals developed typical clinical signs of PPR and the challenged virus was detected in ocular, nasal and oral secretions of these animals. This study demonstrated that a single immunization with PPR vaccine conferred solid protection in sheep and goats for 3 years.

关键词： PPR Vaccine Small Ruminants

Epidemiological Analysis of Peste des Petits Ruminants (PPR) Outbreaks in Pakistan

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Journal of Biosciences and Medicines 2014年第6期2卷 18-26页

作者： Aamer Bin Zahur Aman Ullah Hamid Irshad asma latif Riasat Wasee Ullah Muhammad Jahangir Muhammad Afzal Shahid Ali Khan Sarosh Majid Salaria Animal Health Research Laboratories Animal Sciences Institute National Agricultural Research Centre Park Road Chak Shahzad Islamabad Pakistan FAO-UN Pakistan (GCP/PAK/127/USA) NARC Park Road Islamabad Pakistan Azad Jammu and Kashmir Medical College Muzaffarabad Azad Kashmir

The current study reports the outbreaks of Peste des Petits Ruminants (PPR) in the small ruminant population of Pakistan. The objectives were to understand the clinical picture of disease under field conditions, estimate the basic epidemiological parameters for the local population of small ruminants and to determine the spatial and temporal distribution of PPR during 2005 to 2007 in Pakistan. A total of 62 outbreaks were investigated among sheep and goat flocks in the five provinces of Pakistan and Azad Jammu & Kashmir (AJK). The PPR virus activity in these outbreaks was demonstrated by clinical picture and presence of PPR virus specific antibodies by employing cELISA. The combined estimates of mean cumulative morbidity and mortality for sheep and goats were estimated 65.37% and 26.51% respectively with a case fatality of 40.40%. The species specific mean cumulative morbidity, mortality and case fatality for goats were 68.80%, 29.45% and 42.75% respectively, while these estimates for sheep were 48.77%, 14.98% and of 26.16% respectively. These estimates for goats were significantly higher (P < 0.001 to P = 0.001) than those for sheep. It was concluded that PPR is wide spread throughout the country and epidemiological picture suggest that disease has established as an endemic infection in the country.

关键词： PPR Sheep Goat Cumulative Morbidity Cumulative Mortality Case Fatality

Epidemiological Analysis of Peste des Petits Ruminants(PPR) Outbreaks in Pakistan

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Epidemiological Analysis of Peste des Petits Ruminants(PPR) ...

In silico designing and optimization of anti-epidermal growth factor receptor scaffolds by complementary-determining regions-grafting technique

2014 Conference on Epidemiology and Infectious Diseases(EID2014) & The Healthy and Active Aging Conference(HAAC2014)

The current study reports the outbreaks of Peste des Petits Ruminants(PPR) in the small ruminant population of Pakistan. The objectives were to understand the clinical picture of disease under field conditions, estimate the basic epidemiological parameters for the local population of small ruminants and to determine the spatial and temporal distribution of PPR during 2005 to 2007 in Pakistan. A total of 62 outbreaks were investigated among sheep and goat flocks in the five provinces of Pakistan and Azad Jammu & Kashmir(AJK). The PPR virus activity in these outbreaks was demonstrated by clinical picture and presence of PPR virus specific antibodies by employing c ELISA. The combined estimates of mean cumulative morbidity and mortality for sheep and goats were estimated 65.37% and 26.51% respectively with a case fatality of 40.40%. The species specific mean cumulative morbidity, mortality and case fatality for goats were 68.80%, 29.45% and 42.75% respectively, while these estimates for sheep were 48.77%, 14.98% and of 26.16% respectively. These estimates for goats were significantly higher(P < 0.001 to P = 0.001) than those for sheep. It was concluded that PPR is wide spread throughout the country and epidemiological picture suggest that disease has established as an endemic infection in the country.

关键词： PPR,Sheep,Goat,Cumulative Morbidity,Cumulative Mortality,Case Fatality

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Quantitative Biology 2024年第3期12卷 301-312页

作者： Razieh Rezaei Adriani Seyed latif Mousavi Gargari Hamid Bakherad Jafar Amani Department of Biology Shahed UniversityTehranIran Department of Pharmaceutical Biotechnology School of Pharmacy and Pharmaceutical SciencesIsfahan University of Medical SciencesIsfahanIran Applied Microbiology Research Center System Biologyand Poisonings InstituteBaqiyatallah University of Medical SciencesTehranIran

Monoclonal antibodies are attractive therapeutic agents in a wide range of human disorders that bind specifically to their target through their complementary-determining regions(CDRs).Small proteins with structurally preserved CDRs are promising antibodies *** this in silico study,we presented new antibody mimetics against the cancer marker epidermal growth factor receptor(EGFR)created by the CDRs grafting *** potential graft acceptor sites that efficiently immobilize the grafted CDR loops were selected from three small protein scaffolds using a *** three most involved CDR loops in antibody-receptor interactions extracted from panitumumab antibody against the EGFR domain III crystal structure were then grafted to the selected scaffolds through the loop randomization *** combination of three CDR loops and 10 grafting sites revealed that three of the 36 combinations showed specific binding to EGFR DIII by binding energy ***,the present strategy and selected small protein scaffolds are promising tools in the design of new binders against EGFR with high binding energy.

关键词： CDR grafting technique EGFR molecular dynamic simulation panitumumab

Phosphorus limitation on CO_(2)fertilization effect in tropical forests informed by a coupled biogeochemical model

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Forest Ecosystems 2024年第4期11卷 502-515页

作者： Zhuonan Wang Hanqin Tian Shufen Pan Hao Shi Jia Yang Naishen Liang latif Kalin Christopher Anderson Natural Resources Ecology Laboratory Colorado State UniversityFort CollinsCO80523USA Center for Earth System Science and Global Sustainability Schiller Institute for Integrated Science and SocietyBoston CollegeChestnut HillMAUSA Department of Earth and Environmental Sciences Boston CollegeChestnut HillMAUSA Department of Engineering Boston CollegeChestnut HillMAUSA Research Center for Eco-Environmental Sciences State Key Laboratory of Urban and Regional EcologyChinese Academy of SciencesBeijingChina Department of Natural Resource Ecology and Management Oklahoma State UniversityStillwaterOKUSA Center for Global Environmental Research(CGER) National Institute for Environmental Studies(NIES)TsukubaIbaraki305-8506Japan College of Forestry Wildlife and EnvironmentAuburn UniversityAuburnALUSA

Tropical forests store more than half of the world's terrestrial carbon(C)pool and account for one-third of global net primary productivity(NPP).Many terrestrial biosphere models(TBMs)estimate increased productivity in tropical forests throughout the 21st century due to CO_(2)***,phosphorus(P)liaitations on vegetation photosynthesis and productivity could significantly reduce the CO_(2)fertilization ***,we used a carbon-nitrogen-phosphorus coupled model(Dynamic Land Ecosystem Model;DLEM-CNP)with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests *** model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem *** further compared the simulations from DLEM-CNP,DLEM-CN,and DLEMC and the results showed that the inclusion of P processes reduced the CO_(2)fertilization effect on gross primary production(GPP)by 25%and 45%,and net ecosystem production(NEP)by 28%and 41%,respectively,relative to CN-only and C-on ly *** the 1860s to the 2010s,the DLEM-CNP estimated that in tropical forests GPP increased by 17%,plant respiration(Ra)increased by 18%,ecosystem respiration(Rh)increased by 13%,NEP increased by 121%per unit area,***,factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO_(2) fertilization effect had been offset by 135%due to deforestation from the 1860s to the *** study highlights the importance of P limitation on the C cycle and the weakened CO_(2)fertilization effect resulting from P limitation in tropical forests.

关键词： Tropical forests Carbon-nitrogen-phosphorus model Phosphorus limitation CO_(2)fertilization effect Terrestfial ecosy stem model

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

Peste des Petits Ruminants Vaccine(Nigerian Strain 75/1) Confers Protection for at Least 3 Years in Sheep and Goats

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Peste des Petits Ruminants Vaccine(Nigerian Strain 75/1) Con...

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

2014 Conference on Epidemiology and Infectious Diseases(EID2014) & The Healthy and Active Aging Conference(HAAC2014)

作者： Aamer Bin Zahur Hamid Irshad Aman Ullah Muhammad Afzal asma latif Riasat Wasee Ullah Umer Farooq Muhammad Humayoon Samo Muhammad Jahangir Giancarlo Ferrari Manzoor Hussain M.Munir Ahmad Animal Health Research Laboratories Animal Sciences Institute National Agricultural Research Centre FAO-UN Pakistan (GCP/PAK/127/USA) NARC Park Road Islamabad Pakistan AGAH FAO (Hq) Rome Italy Divisional Diagnostic Laboratory Livestock and Dairy Development Department

The present study reports the duration of immunity and protective efficacy of Peste des Petits Ruminants(PPR) vaccine(Nigerian strain 75/1) in sheep and goats. A total of 105 sheep and goats were divided into three groups A, B and C. Group A received normal recommended dose(1.0 ml) of PPR vaccine, group B received half dose(0.5 ml) of PPR vaccine and group C was kept as unvaccinated control group in contact with vaccinated animals. The post vaccination dynamics of antibodies against PPR virus was studied. It was found that significant antibody titres persisted for 3 years post vaccination in sheep and goats vaccinated with either full dose or half dose of PPR vaccine. The challenge protection studies were carried out in experimental animals at 24 and 36 month post vaccination. The vaccinates withstood challenge and were found completely resistant clinically and virologically to virulent PPR virus for 24 and 36 months post vaccination. The unvaccinated control animals developed typical clinical signs of PPR and the challenged virus was detected in ocular, nasal and oral secretions of these animals. This study demonstrated that a single immunization with PPR vaccine conferred solid protection in sheep and goats for 3 years.

关键词： PPR Vaccine,Small Ruminants

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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Intelligent Automation & Soft Computing 2024年第1期39卷 57-71页

作者： Ghulam Gilanie Sana Cheema Akkasha latif AnumSaher Muhammad Ahsan Hafeez Ullah Diya Oommen Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan West High School Salt Lake CityUT84103USA

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the *** affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of ***,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of *** this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based *** Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry *** have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental *** experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,***,the outcomes demonstrated the stated method’s superiority over the current best practices.

关键词： Bipolar mental illness detection facial micro-expressions facial landmarked images