Tech Science Press
Not a member yet
3972 research outputs found
Sort by
Trichoderma spp. and Bacillus spp. as growth promoters in maize (Zea mays L.)
No full textMicrobes that are beneficial to plants are used to
enhance the crop growth, yield and are alternatives to chemical
fertilizers. Trichoderma and Bacillus are the predominant plant
growth-promoting fungi and bacteria. The objective of this study
was select, characterize, and evaluate isolates of Trichoderma
spp. and Bacillus spp. native from the northern region of Sinaloa,
Mexico, and assess their effect on growth promotion in maize (Zea
mays L.). In greenhouse conditions, four Trichoderma isolates and
twenty Bacillus isolates, as well as two controls, were tested in a
completely randomized design with three replicates. We selected
the two best strains of Trichoderma and Bacillus: TB = Trichoderma
asperellum, TF = Trichoderma virens, B14 = Bacillus cereus sensu
lato and B17 = Bacillus cereus, which were evaluated in the field in
a completely randomized blocks in factorial arrangement design
with three replicates applying different rates of nitrogen fertilizer
(0, 150 kg N/ha, and 300 kg N/ha). Treatments 5 (B17 = B. cereus)
and 11 (TF = T. virens) both fertilized with 150 kg N/ha showed
similar yields and they did not reveal significant differences from
the treatments fertilized with 300 kg N/ha. This indicated that
treatment 5 (B17= B. cereus with 150 kg N/ha) and treatment
11 (TF= T. virens with 150 kg N/ha) were efficient as growth
promoters, by not showing significant differences in root volume
and dry weight of foliage. The results indicated a reduction of 50%
in the rate of nitrogen to fertilizer required for maize (Zea mays
L.) crops. These microorganisms Trichoderma and Bacillus could
be an alternative to reduce the use of chemical fertilizers in maize
<i>XRCC1</i> Arg399Gln and Arg194Trp polymorphisms regulate XRCC1 expression and chemoresistance of non-small cell lung cancer cells
No full textX-ray repair cross-complementing protein 1 (XRCC1) could repair cisplatin-induced DNA damage. XRCC1
Arg399Gln and Arg194Trp variants alter XRCC1 expression and function, leading to changes in cancer sensitivity to
cisplatin treatment. This study aimed to investigate the effects of XRCC1 Arg399Gln and Arg194Trp polymorphisms
on cell viability, apoptosis and XRCC1 expression in cisplatin-sensitive A549 and cisplatin-resistant A549/DDP nonsmall cell lung cancer (NSCLC) cells. Plasmids carrying XRCC1 Arg399Gln and Arg194Trp were constructed and
transfected into A549 and A549/DDP cells. RT–PCR, Western blot, MTT assay, and flow cytometry analysis were
performed to assess cell viability, apoptosis, and XRCC1 expression. Compared to control cells, the viability of A549
and A549/DDP cells transfected with XRCC1 Arg399Gln and Arg194Trp was higher and the apoptosis rate was lower,
and XRCC1 mRNA and protein expression levels were significantly higher. In conclusion, our results suggest that
XRCC1 Arg399Gln and Arg194Trp polymorphisms change XRCC1 expression in NSCLC cells and alter the sensitivity
of NSCLC to cisplatin-based chemotherap
Histopathological patterns of ovarian lesions: A study of 161 cases
No full textOvarian lesions are commonly encountered pathologies that cannot be categorized clinicoradiologically.
Definite diagnosis is of great importance for therapeutic and prognostic purposes. Histopathology gives accurate
diagnosis in most cases. Few cases need supportive tests like immunohistochemistry. Objective: to study the
histomorphological diversity of ovarian lesions, their age and location in North of Iraq (Mosul and Duhok). Patients
and methods: In the period extended from January 2008 to December 2011, 161 cases of ovarian lesions were
collected from pathology departments in Azadi General Hospital “Duhok” and Al-Jamhori Teaching Hospital “Mosul”.
Automated tissue processor was used for histologic study and Streptavidin-biotin method on paraffin sections was
applied for immunohistochemistry. Result: There was a wide age range, most being in the third decade. The right
ovaries were more common involved than the left. Histologically, 58 (36%) cases were non-neoplastic and 103 were
neoplastic including 90 (55.9%) benign and 9 (5.6%) malignant tumors. The remaining 4 (2.4%) cases comprised
borderline serous cystadenoma. Conclusion: Most ovarian lesions were functional non-neoplastic followed by benign
neoplastic. Apart from few cases, diagnosis was merely histological without any ancillary test
Morphometric and Biochemical Changes in <i>Agave americana</i> L. Plantlets Induced By Ethyl Methanesulfonate
No full textA. americana L. is a crop with very little genetic variability. In order to evaluate the effect of ethyl methanesulfonate (EMS) to induce variability in in vitro plantlets of A. americana, different explants (meristems, leaves and roots) were evaluated for the production of callus. MS medium supplemented with ANA (2.68 μM) and BAP (2.68 μM) was used. Callus obtained from apical meristem were treated with 15 mM EMS for two hours after which shoot formation was induced using 2,4-D (0.11 μM) and BAP (44 μM). The EMS induced variations in the morphometric and morphological parameters of the plantlets obtained, with 60% of the plantlets presenting differences such as dwarfism and different leaf forms, without the presence of spines, as well as an increase in fructan content of 30% with respect to the control plantlets. PAL was increased and this activity is related with higher anthocyanins concentration in A. americana L. plantlets
A New Time-Aware Collaborative Filtering Intelligent Recommendation System
No full textAiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality
Long Short Term Memory Networks Based Anomaly Detection for KPIs
No full textIn real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their business. However, anomaly detection for these data with various patterns and data quality has been a great challenge, especially without labels. In this paper, we adopt an anomaly detection algorithm based on Long Short-Term Memory (LSTM) Network in terms of reconstructing KPIs and predicting KPIs. They use the reconstruction error and prediction error respectively as the criteria for judging anomalies, and we test our method with real data from a company in the insurance industry and achieved good performance
Efficient Construction of B-Spline Curves with Minimal Internal Energy
Get PDFIn this paper, we propose an efficient method to construct energy-minimizing B-spline curves by using discrete mask method. The linear relations between control points are firstly derived for different energy-minimization problems, then the construction of B-spline curve with minimal internal energy can be addressed by solving a sparse linear system. The existence and uniqueness of the solution for the linear system are also proved. Experimental results show the efficiency of the proposed approach, and its application in G1 blending curve construction is also presented
High Precision SAR ADC Using CNTFET for Internet of Things
Get PDFA high precision 10-bit successive approximation register analog to digital converter (ADC) designed and implemented in 32nm CNTFET process technology at the supply of 0.6V, with 73.24 dB SNDR at a sampling rate of 640 MS/s with the average power consumption of 120.2 μW for the Internet of things node. The key components in CNTFET SAR ADCs are binary scaled charge redistribution digital to analog converter using MOS capacitors, CNTFET based dynamic latch comparator and simple SAR digital code error correction logic. These techniques are used to increase the sampling rate and precision while ensuring the linearity, power consumption and noise level are within the limit. The proposed architecture has high scalability to CNTFET technology and also has higher energy efficiency. We compared the results of CNTFET based SAR ADC with other known architectures and confirm that this proposed SAR ADC can provide higher precision, power efficiency to the Internet of things node
GaiaWorld: A Novel Blockchain System Based on Competitive PoS Consensus Mechanism
Get PDFThe birth of blockchain has promoted the development of electronic currencies such as Bitcoin and Ethereum. Blockchain builds a financial system based on cryptology instead of credit, which allows parties to complete the transaction on their own without the need for credible third-party intermediaries. So far, the application scenario of blockchain is mainly confined to the peer-to-peer electronic financial system, which obviously does not fully utilize the potential of blockchain.
In this paper, we introduce GaiaWorld, a new system for decentralized application. To solve the problem of resource waste and mismatch between nodes and computing power in traditional PoW mechanism, GaiaWorld introduces a new consensus mechanism called CPoS, which can improve productivity and liquidity of blockchain system. GaiaWorld constructs a new architecture based on forging committee and forging group systems, which can establish a decentralized, free and stable internet trust system, and can be utilized in multiple application scenarios and construct efficient and reliable content delivery systems
A Recommendation System Based on Fusing Boosting Model and DNN Model
Get PDFIn recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can construct new features related before and after tree. Convolutional neural networks have a better perception of local features. In this paper, we take advantage of convolutional networks to capture the local features. The features are constructed by the node leaf of gradient boosting decision tree. This paper employs the tree leaf node to mine the user behavior path features, and uses the deep model to extract the user abstract features. Based on a Kaggle competition, our model performs better in the test data than any other model