Showing papers by "Muhammad Hassan published in 2017"

A novel zoonotic Anaplasma species is prevalent in small ruminants: potential public health implications.

Abstract: Tick-borne diseases currently represent an important issue for global health A number of emerging tick-transmitted microbes continue to be discovered, and some of these are already identified as the cause of human infections Over the past two decades, Anaplasma phagocytophilum is considered to be mainly responsible for human anaplasmosis However, a novel zoonotic pathogen provisionally named “Anaplasma capra” has recently been identified in China In this study, we did an active surveillance of A capra in goats and sheep in different geographical regions of China The presence of A capra was determined by nested PCR in 547 blood samples collected from goats and sheep from 24 counties distributed in 12 provinces in China The molecular characterization of A capra isolates in sheep and goats was achieved based on four conventional genetic markers (16S rRNA, gltA, groEL and msp4 genes) Anaplasma capra was identified in 75 of 547 animals, with an overall prevalence of 137% The infection rates in the survey sites ranged from 0 to 786%, and were significantly different (P < 001) Phylogenetic analysis revealed that the isolates obtained from goats, sheep, Ixodes persulcatus ticks and humans create a separate clade within the genus Anaplasma and distinct from other recognized Anaplasma species These findings indicated that these A capra isolates possess the same molecular characteristics, suggesting that this organism could be a substantial health threat to both animals and humans Anaplasma capra is an emerging tick-transmitted zoonotic pathogen This novel Anaplasna species is widespread across China with an overall prevalence of 137% in goats and sheep with isolates indistinguishable from those found in humans These findings warrant increased public health awareness for human anaplasmosis

Characterization of Anaplasma ovis strains using the major surface protein 1a repeat sequences.

Abstract: Anaplasma ovis is one of the tick-transmitted pathogens of small ruminants It causes ovine anaplasmosis and widely distributed in the world In contrast to extensive worldwide genetic diversity of A marginale and A phagocytophilum, there are few reports on the classification of A ovis strains This study was conducted to investigate the occurrence and characterize A ovis strains from goats and sheep from 12 provinces in China The occurrence of A ovis DNA was tested in 552 goats and sheep, by PCR based on the msp4 gene Positive samples were used for the amplification of the msp1a gene of A ovis The Msp1a amino acid repeats were further identified and used for the characterization of A ovis strains The results showed that 79 (143%) goats and sheep were positive for A ovis The infection rates of A ovis among different study sites ranged from 0 to 100%, and were significantly higher in sheep (266%, 45/169) than in goats (89%, 34/383) (χ 2 = 21403, df = 1, P < 0001) The msp4 gene sequences of these isolates were 998–100% identical to each other, and they represented two sequence types Forty-four partial msp1a gene sequences containing the repeat sequences were obtained from A ovis-positive samples After translation to amino acid sequences, 24 Msp1a repeats with 33 to 47 amino acids, which corresponded to 19 genotypes of A ovis, were recognized in goats and sheep in China Anaplasma ovis is widely distributed in the investigated geographical regions The msp4 gene of A ovis had high sequence identity and was unable to be used to discriminate different strains The Msp1a could be used as a genetic marker for characterizing A ovis, and 19 genotypes of A ovis were recognized in domestic small ruminants in China The present study revealed, for the first time, the genetic diversity of A ovis based on the analyses of Msp1a amino acid repeats

Ultrathin Tungsten Oxide Nanowires/Reduced Graphene Oxide Composites for Toluene Sensing.

Abstract: Graphene-based composites have gained great attention in the field of gas sensor fabrication due to their higher surface area with additional functional groups. Decorating one-dimensional (1D) semiconductor nanomaterials on graphene also show potential benefits in gas sensing applications. Here we demonstrate the one-pot and low cost synthesis of W18O49 NWs/rGO composites with different amount of reduced graphene oxide (rGO) which show excellent gas-sensing properties towards toluene and strong dependence on their chemical composition. As compared to pure W18O49 NWs, an improved gas sensing response (2.8 times higher) was achieved in case of W18O49 NWs composite with 0.5 wt. % rGO. Promisingly, this strategy can be extended to prepare other nanowire based composites with excellent gas-sensing performance.

Identification of 12 Piroplasms Infecting Ten Tick Species in China Using Reverse Line Blot Hybridization.

Abstract: Piroplasmosis, a disease of domestic and wild animals, is caused by tick-borne protozoa in the genera of Theileria and Babesia. There is limited information available about the prevalence of piroplasmosis in ticks in China, and to assess the potential threat of piroplasmosis in China, we investigated the infections of ovine and bovine Babesia and Theileria species in ticks collected from cattle, yaks, sheep, horses, and camels in several regions of China where tick-borne diseases have been reported. In total, 652 ticks were collected from the animals in 6 provinces of China. Babesia spp. and Theileria spp. were detected with a PCR-RLB method and identified by sequencing. Overall, 157 ticks (24.1%) were infected with 5 Babesia and 4 Theileria species. Among tested tick samples, 134 (20.6%) were single infections with 1 of 7 piroplasm species, with Theileria annulata (118/652, 18.1%) being dominant. Only 23 (3.5%) tick samples were double or triple infected, Theileria luwenshuni and Theileria sinen...

Tool path optimal design for slow tool servo turning of complex optical surface

Abstract: In order to increase the machining accuracy of slow tool servo turning of complex optical surface, the optimal design for tool path was studied. A comprehensive tool path generation strategy was proposed to optimize the tool path for machining complex surfaces. A new algorithm was designed for tool nose radius compensation which had less calculation error. Hermite segment interpolation was analyzed based on integrated multi-axes controller, and a new interpolation method referred to as triangle rotary method was put forward and was compared with the area method and three-point method. The machining simulation indicated that the triangle rotary method was significant in error reduction. The interpolation error of toric surface was reduced to 0.0015 µm from 0.06 µm and sinusoidal array surface’s interpolation error decreases to 0.37 µm from 1.5 µm. Finally, a toric surface was machined using optimum tool path generation method to evaluate the proposed tool path generation method.

Methane Enhancement through Sequential Thermochemical and Sonication Pretreatment for Corn Stover with Anaerobic Sludge

Abstract: The present research emphasized the utilization of a novel sequential thermochemical and sonication pretreatment technology to enhance methane production from corn stover The corn stover was thermochemically pretreated with sodium hydroxide to enhance its lignocellulosic digestibility Due to thermochemical pretreatment, 6545% lignin removal and 3633% hemicellulose solubilization was observed and further five sonication levels were employed (25, 45, 60, 90, and 120 min) All pretreatments were found significant (P < 005) to enhance methane production from 1478% to 7372% while thermo-NaOH pretreatment with 90 min sonication time was proven as the optimum pretreatment with specific methane production of 320 mL/g volatile solids (VS) Anaerobic digestion process stability was deeply monitored at 3 day intervals via total volitile fatty acids, alcohol production, pH, chemical oxygen demand, and VS removal

Modeling and Multiresponse Optimization for Anaerobic Codigestion of Oil Refinery Wastewater and Chicken Manure by Using Artificial Neural Network and the Taguchi Method.

Abstract: To study the optimum process conditions for pretreatments and anaerobic codigestion of oil refinery wastewater (ORWW) with chicken manure, L9 (34) Taguchi's orthogonal array was applied. The biogas production (BGP), biomethane content (BMP), and chemical oxygen demand solubilization (CODS) in stabilization rate were evaluated as the process outputs. The optimum conditions were obtained by using Design Expert software (Version 7.0.0). The results indicated that the optimum conditions could be achieved with 44% ORWW, 36°C temperature, 30 min sonication, and 6% TS in the digester. The optimum BGP, BMP, and CODS removal rates by using the optimum conditions were 294.76 mL/gVS, 151.95 mL/gVS, and 70.22%, respectively, as concluded by the experimental results. In addition, the artificial neural network (ANN) technique was implemented to develop an ANN model for predicting BGP yield and BMP content. The Levenberg-Marquardt algorithm was utilized to train ANN, and the architecture of 9-19-2 for the ANN model was obtained.

Anaerobic co-digestion of oil refinery wastewater and chicken manure to produce biogas, and kinetic parameters determination in batch reactors

Abstract: In order to improve the anaerobic fermentation of oil refinery wastewater (ORWW) via an appropriate nutrients pool for microbial and buffer capacity growth, a study was carried out on related anaerobic co-digestion (AcoD) with a rich organic carbon source, namely chicken manure (CM). The kinetic parameters were investigated (including cumulative biogas production, bio-methane content, retention time, and soluble chemical oxygen demand stabilisation rate) of batch AcoD experiments related to six ORWW:CM-ratio treatments (5:0, 4:1, 3:2, 2:3, 1:4, and 0:5) under mesophilic conditions. The highest soluble chemical oxygen demand removal rate was obtained for the 4:1-ratio treatment. However, the highest biogas production and bio-methane contents were achieved for the 1:4-ratio treatment. When taking into consideration the highest oil refinery wastewater portion in the AcoD mixtures and the statistical test results (LSD0.05) for the kinetic parameters, it can be seen that the 4:1-ratio treatment provided the maximum biogas

Anaerobic Co-Digestion of Oil Refinery Wastewater with Bagasse; Evaluating and Modeling by Neural Network Algorithms and Mathematical Equations

Abstract: To survey the anaerobic co-digestion (AcoD) of oil refinery wastewater (ORWW) with sugarcane bagasse (SCB), six different AcoD compositions were evaluated. Results including cumulative biogas production (BGP), bio-methane contents (BMP), and soluble chemical oxygen demand (CODs) removal rate were experimentally obtained. The negligible BGP by ORWW mono-digestion revealed that it could not support any microbial activity. However, increasing the SCB ratio in the AcoD compositions led to increased BGP and BMP contents. By considering the statistical test (LSD0.05) results for the kinetic parameters, the 1:4 ratio treatment was the most favorable AcoD composition. Moreover, the CODs removal rate from 22.34 ± 1.63% for the SCB mono-digestion was improved to 49.67 ± 0.38% for the 2:3 AcoD composition and BMP content from 54.12 ± 0.45% for the SCB mono-digestion was enhanced to 62.69 ± 1.22% for the 1:4 AcoD composition with 20% lower SCB usage. The results computed by applying three mathematical models determined that the modified Gompertz model provided the best fit. Also, implementing artificial neural network algorithms to model the BGP data revealed that the Back Propagation algorithm was the best suited for the experimental BGP data, with 0.6444 and 0.9658 for MSE and R2, respectively.

Computationally Efficient Weighted Binary Decision Codes for Gas Identification With Array of Gas Sensors

Abstract: Motivated by biological olfactory coding principles, rank-code-based classifiers have recently been proposed to facilitate integration of hardware-friendly gas identification platforms with an array of gas sensors. These classifiers operate on a simple principle of generating rank-codes by ranking the gas sensors’ features instead of treating them as a multi-dimensional vector as in computation-intensive state-of-the-art gas classifiers. However, the performance of the rank-code-based classifiers is limited when distinguishing information about all the target gases is not found in the ranks of the gas sensors’ features, but in their values. In this paper, we introduce a computationally efficient alternative solution to overcome this limitation. In this solution, an original multi-gas classification problem is decomposed into pairwise classifiers, and the gas is then predicted with the weighted binary decision codes in each of these classifiers, where each element of the code is generated by exploiting features individually. The weighted binary decision codes are formed by first using the nearest centroid approach, which exploits the mean value of each gas sensor’s feature to generate binary decision codes, and then, a simple approach is used to assign a weight to each element of the code, depending upon its capability to discriminate the gases in each pairwise classifier. The added advantage of this classification approach is that two computationally efficient metrics are introduced to access the classifiers’ applicability to the given data set and certainty about the prediction of any test sample. A classification performance of 97.87% is achieved with this approach on an extensive data set of ten gases experimentally obtained with Figaro series gas sensors, and this is increased to 100% by rejecting 3.37% of samples for which certainty about their predictions is below a 25% confidence level.

Impact of tillage operation on soil physical, mechanical and rhelogical properties of paddy soil

Abstract: Mari, I. A., Ji Changying, N. Leghari, F. A. Chandio, Ch. Arslan and M. Hassan. Impact of tillage operation on soil physical, mechanical and rhelogical properties of paddy soil. Bulg. J. Agric. Sci., 21: 940–946 Laboratory experiment was conducted on paddy soil to determine the effect of tillage operations before and after on soil physical, rheological and mechanical properties. Tillage operations play an important role to change the physical and mechanical properties of the soil. The three soil moisture content levels (27%, 30% and 33%) were used in the study to measure the effect of tillage operation on soil conditions. Results showed that soil bulk density was observed directly proportional to the soil moisture while soil porosity was inversely proportional to the moisture content. It is observed that soil requires more shear stress before tillage operation. Overall results showed that the soil water content has great impact on soil properties. A negative impact of tillage operation was found in soil bulk density and internal friction angle, while decreasing trend was found on porosity, shear stress, penetration resistance after tillage operation significantly (P = < 0.01). Soil water content on rheological parameters (Em, Ek, λm and λk) were found predominantly significant (p = < 0.01).