Development and validation of a rapid turbidimetric assay to determine the potency of ampicillin sodium in powder for injectable solution

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Maintenance of Manioc Diversity by Traditional Farmers in the State of Mato Grosso, Brazil: A 20-Year Comparison

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Brief Report: The lincRNA Hotair Is Required for Epithelial-to-Mesenchymal Transition and Stemness Maintenance of Cancer Cell Lines

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Body Composition and Net Energy Requirements for Maintenance of Non-Castrated Water Buffaloes (Bubalus bubalis)

The research aimed to estimate body contents of protein and energy and net requirements of energy for maintenance of buffaloes, slaughtered at different stages of maturity. There were used 14 Mediterranean intact males with initial average body weight of 352.2 +/- 24.3 kg and average age of 24 months. The animais were randomly divided into four experimental groups. One group was designed to slaughter at the beginning of the experimental period (IS). The animals of another group were restricting fed, receiving, individually, levels of protein and energy 15% above maintenance (RF). The animals of the two remaining groups were individually fed ad libitum (SW450 and SW500) to reach weights corresponding to 100 and 110 percent of the mature weight of the buffalo cows (respectively 450 and 550 kg). The ration contained ground-corn cobs, soybean meal, urea, minerals, and signal-grass (Brachiaria decumbens) hay, with a concentrate: roughage ratio of 50: 50 and 13% of crude protein on a dry matter basis. To estimate changes in body composition inside the range of weights included in the trial, linear regression equations of log protein (kg), fat (kg) and energy (Mcal) as a function of log empty-body-weight (EBW), in kg, were fitted. Energy requirements for maintenance were obtained as estimated heat production at zero level of energy intake. Buffaloes submitted to fattening in feedlot presented early body fat deposition, and had with the same live weight lower protein content and higher fat content and energy per unit weight than european-zebu crossbred cattle.

The Role of Implant/Abutment System on Torque Maintenance of Retention Screws and Vertical Misfit of Implant-Supported Crowns Before and After Mechanical Cycling

Purpose: This study aimed to evaluate the role of the implant/abutment system on torque maintenance of titanium retention screws and the vertical misfit of screw-retained implant-supported crowns before and after mechanical cycling. Materials and Methods: Three groups were studied: morse taper implants with conical abutments (MTC group), external-hexagon implants with conical abutments (EHC group), and external-hexagon implants with UCLA abutments (EHU group). Metallic crowns casted in cobalt-chromium alloy were used (n = 10). Retention screws received insertion torque and, after 3 minutes, initial detorque was measured. Crowns were retightened and submitted to cyclic loading testing under oblique loading (30 degrees) of 130 +/- 10 N at 2 Hz of frequency, totaling 1 x 10(6) cycles. After cycling, final detorque was measured. Vertical misfit was measured using a stereomicroscope. Data were analyzed by analysis of variance, Tukey test, and Pearson correlation test (P < .05). Results: All detorque values were lower than the insertion torque both before and after mechanical cycling. No statistically significant difference was observed among groups before mechanical cycling. After mechanical cycling, a statistically significantly lower loss of detorque was verified in the MTC group in comparison to the EHC group. Significantly lower vertical misfit values were noted after mechanical cycling but there was no difference among groups. There was no significant correlation between detorque values and vertical misfit. Conclusions: All groups presented a significant decrease of torque before and after mechanical cycling. The morse taper connection promoted the highest torque maintenance. Mechanical cycling reduced the vertical misfit of all groups, although no significant correlation between vertical misfit and torque loss was found.

Energy requirements for maintenance and growth of Caninde goat kids

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Predictive maintenance of a worm reducer with an unbalanced load

Among all predictive maintenance techniques the oil analysis and vibration analysis are the most important for monitoring some mechanical systems. The integration of these techniques has potential to improve industrial maintenance practices and provide a better economic gain for industries. To study the integration of these two techniques, a test rig was set up to obtain an extreme working condition for the worm reducer used in this paper. The test rig was composed by a motor connected to a reducer through a flexible coupling and with an unbalanced load. The analysis of the results carried out by using a sample of the oil recommended by the manufacturer in extreme conditions, and using liquid contaminant is presented. From the results it was observed that if there is an abnormal instantaneous load in a system, the subsequent vibration analysis may not perceive what occurred if there was no permanent damage, which is not the case with the lubricant analysis.

Energy Requirements for Maintenance and Growth of Male Saanen Goat Kids

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Human follicle stimulating hormone (hFSH) and thyroxine (T4) in survival maintenance and in vitro growth promotion of caprine preantral follicles

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Particle competition and cooperation for semi-supervised learning with label noise

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mineral requirements for growth and maintenance of F-1 Boer x Saanen male kids

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.