3D Printing of Microgel-Loaded Modular Microcages as Instructive Scaffolds for Tissue Engineering

Biomaterial scaffolds have served as the foundation of tissue engineering and regenerative medicine. However, scaffold systems are often difficult to scale in size or shape in order to fit defect-specific dimensions, and thus provide only limited spatiotemporal control of therapeutic delivery and host tissue responses. Here, a lithography-based 3D printing strategy is used to fabricate a novel miniaturized modular microcage scaffold system, which can be assembled and scaled manually with ease. Scalability is based on an intuitive concept of stacking modules, like conventional toy interlocking plastic blocks, allowing for literally thousands of potential geometric configurations, and without the need for specialized equipment. Moreover, the modular hollow-microcage design allows each unit to be loaded with biologic cargo of different compositions, thus enabling controllable and easy patterning of therapeutics within the material in 3D. In summary, the concept of miniaturized microcage designs with such straight-forward assembly and scalability, as well as controllable loading properties, is a flexible platform that can be extended to a wide range of materials for improved biological performance.     

A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks

The Time-delay Added Evolutionary Forecasting (TAEF) approach is a new method for time series prediction that performs an evolutionary search for the minimum number of dimensions necessary to represent the underlying information that generates the time series. The methodology proposed is inspired in Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network combined with a modified genetic algorithm. Initially, the TAEF method finds the best fitted model to forecast the series and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of some series. An experimental investigation conducted with relevant time series show the robustness of the method through a comparison, according to several performance measures, to previous results found in the literature and those obtained with more traditional methods.     

Analytical optimal solution of the extension of the depth of field using cubic-phase wavefront coding. Part I. Reduced-complexity approximate representation of the modulation transfer function

In this paper we derive an approximate analytical representation for the modulation transfer function (MTF) of an imaging system possessing a defocused cubic-phase pupil function. This expression is based on an approximation using the Arctan function and significantly reduces the computational time required to calculate the resulting MTF. We derive rigorous bounds on the minimum and average accuracy of our approximation. Using this approximate representation of the MTF, the analytical solution of the problem of calculating the extension of the depth of field for a circular aperture with a cubic phase mask becomes possible. We also comment on how one can modify our method to construct a lower-bound or an upper-bound approximate analytical expression for the MTF.     

Biological nitrogen fixation in tropical dry forests with different legume diversity and abundance

Tropical dry forests have high diversity and plant abundance of potentially biologically nitrogen fixing (BNF) legume species, attributed to the ecological advantage of fixation. However, there are few estimates of N quantities annually fixed, hindering the understanding of factors that control BNF, like low phosphorus availability. The quantities of N fixed in three dry forest (caatinga) fragments of the semiarid Brazilian northeastern region with different legume plant proportions were determined and seedlings of Mimosa tenuiflora were grown with phosphorous fertilized soil from the fragments to verify if lack of fixation was due to the absence of rhizobia populations or P deficiency. The vegetation of all areas was dominated by legume plants, mainly potentially nodulating ones, despite the relatively high soil N availability. M. tenuiflora was the most abundant nodulating legume in all fragments, with annual leaf productions from 800 to 1400 kg ha^?1. BNF amounts were low (1.4, 18 and 3.6 kg ha^?1 year^?1 in the mature caatinga of Petrolina and in the mature and regenerating caatingas of São João, respectively) considering the high proportions of potentially nodulating plants (33, 61 and 82% of total plant basal area), because 80, 10 and 70% of these plants were not fixing and those fixing had only 20–46% of their N derived from the atmosphere. Since the pot grown seedlings nodulated abundantly, the low BNF could not be explained by absence of microsymbionts but likely to low symbiosis efficiency due to relatively high N and low P availability.     

Phosphorus and potassium cycling in a long-term no-till integrated soybean-beef cattle production system under different grazing intensities insubtropics

Long-term integrated crop-livestock system enables constant and more efficient nutrient cycling because animal, pasture and crop residues release nutrients at different rates. Therefore, appropriate management of these systems is needed to maximize benefits from nutrient cycling. The objective of this study was to evaluate how grazing intensity affected the release rates of phosphorus (P) and potassium (K) in pasture, dung and soybean residues in a no-till long-term integrated crop-livestock system. The experiment was established in 2001 on a clayey Oxisol after soybean harvest. Treatments consisted of pasture with sward heights maintained at 1020, 30 and 40 cm by different cattle stocking rates and a non-grazed (NG) treatment. Decomposition and release rates of nutrients in the pasture and dung were determined using litter bags, which were installed at soybean seeding and pasture seeding during two pasture-crop cycles (2009–2011). Lighter grazing intensities resulted in greater P release rate from pasture and dung residues. Pasture and dung residues released K at a very high rate and were not influenced by grazing intensity. The P and K released from soybean residue were not affected by grazing intensity; however, decomposition of soybean leaves was greater than of stems. Greatest rates of total P and K released were from pasture and dung residues under lighter grazing intensities and in the NG areas. Large amounts of P (~25 kg ha^?1) and K (~130–180 kg ha^?1) were cycled in a complete soybean-beef cattle integrated system and must be considered in the fertilization management.     

Accumulation and apparent recovery of N,P and K after the incorporation of gliricidia and manure in intercropping during the cultivation of corn–cowpea–cotton

Fertilization with manure is widely adopted by farmers in the semiarid region of northeastern Brazil, although the quantity of available manure is limited. Thus, among other alternatives, gliricidia has been used as an additional source of organic fertilizer. The objective of this study was to evaluate the accumulation and apparent recovery of N, P and K after using different forms of gliricidia and manure application in the intercropping of corn, cowpea and cotton in three crop cycles in the semi-arid northeast region of Brazil. The following treatments were used: gliricidia incorporated into the soil before planting; gliricidia spread on the surface of the soil at 45 days after planting; manure and gliricidia incorporated into the soil before planting; manure incorporated into the soil before planting and gliricidia spread on the surface of the soil at 45 days after planting; manure incorporated into the soil before planting; and no addition of organic fertilizer (control). Accumulations followed the same pattern as biomass production but differed greatly from the concentrations. Low apparent recoveries were obtained in the first and second cycles, and high apparent recoveries were obtained in the third cycle. The nutrient balances indicated that the incorporation of gliricidia before planting or spread on the surface does not meet crop needs. The incorporation of manure as well as manure and gliricidia when applied on the surface, were able to meet growth needs and maintain soil fertility.     

Uncertainty quantification through the Monte Carlo method in a cloud computing setting

The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows its use in simulations where the computation of a single realization is very costly. This work presents a methodology for the parallelization of the MC method, in the context of cloud computing. This strategy is based on the MapReduce paradigm, and allows an efficient distribution of tasks in the cloud. This methodology is illustrated on a problem of structural dynamics that is subject to uncertainties. The results show that the technique is capable of producing good results concerning statistical moments of low order. It is shown that even a simple problem may require many realizations for convergence of histograms, which makes the cloud computing strategy very attractive (due to its high scalability capacity and low-cost). Additionally, the results regarding the time of processing and storage space usage allow one to qualify this new methodology as a solution for simulations that require a number of MC realizations beyond the standard.     

A multi-modal approach for activity classification and fall detection

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.     

Influence of pressure-relief insoles developed for loaded gait (backpackers and obese people) on plantar pressure distribution and ground reaction forces

The aims of this study were to test the effects of two pressure relief insoles developed for backpackers and obese people on the ground reaction forces (GRF) and plantar pressure peaks during gait; and to compare the GRF and plantar pressures among normal-weight, backpackers, and obese participants. Based on GRF, plantar pressures, and finite element analysis two insoles were manufactured: flat cork-based insole with (i) corkgel in the rearfoot and forefoot (SLS1) and with (ii) poron foam in the great toe and lateral forefoot (SLS2). Gait data were recorded from 21 normal-weight/backpackers and 10 obese participants. The SLS1 did not influence the GRF, but it relieved the pressure peaks for both backpackers and obese participants. In SLS2 the load acceptance GRF peak was lower; however, it did not reduce the plantar pressure peaks. The GRF and plantar pressure gait pattern were different among the normal-weight, backpackers and obese participants.     

Online dispute resolution: an artificial intelligence perspective

Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.