Redes-em-Chip : arquiteturas e modelos para avaliação de área e desempenho

Com o advento dos processos submicrônicos, a capacidade de integração de transistores tem atingido níveis que possibilitam a construção de um sistema completo em uma única pastilha de silício. Esses sistemas, denominados sistemas integrados, baseiam-se no reuso de blocos previamente projetados e verificados, os quais são chamados de núcleos ou blocos de propriedade intelectual. Os sistemas integrados atuais incluem algumas poucas dezenas de núcleos, os quais são interconectados por meio de arquiteturas de comunicação baseadas em estruturas dedicadas de canais ponto-a-ponto ou em estruturas reutilizáveis constituídas por canais multiponto, denominadas barramentos. Os futuros sistemas integrados irão incluir de dezenas a centenas de núcleos em um mesmo chip com até alguns bilhões de transistores, sendo que, para atender às pressões do mercado e amortizar os custos de projeto entre vários sistemas, é importante que todos os seus componentes sejam reutilizáveis, incluindo a arquitetura de comunicação. Das arquiteturas utilizadas atualmente, o barramento é a única que oferece reusabilidade. Porém, o seu desempenho em comunicação e o seu consumo de energia degradam com o crescimento do sistema. Para atender aos requisitos dos futuros sistemas integrados, uma nova alternativa de arquitetura de comunicação tem sido proposta na comunidade acadêmica. Essa arquitetura, denominada rede-em-chip, baseia-se nos conceitos utilizados nas redes de interconexão para computadores paralelos. Esta tese se situa nesse contexto e apresenta uma arquitetura de rede-em-chip e um conjunto de modelos para a avaliação de área e desempenho de arquiteturas de comunicação para sistemas integrados. A arquitetura apresentada é denominada SoCIN (System-on-Chip Interconnection Network) e apresenta como diferencial o fato de poder ser dimensionada de modo a atender a requisitos de custo e desempenho da aplicação alvo. Os modelos desenvolvidos permitem a estimativa em alto nível da área em silício e do desempenho de arquiteturas de comunicação do tipo barramento e rede-em-chip. São apresentados resultados que demonstram a efetividade das redes-em-chip e indicam as condições que definem a aplicabilidade das mesmas.

Effects of Aedes aegypti salivary components on dendritic cell and lymphocyte biology

Abstract Background Saliva is a key element of interaction between hematophagous mosquitoes and their vertebrate hosts. In addition to allowing a successful blood meal by neutralizing or delaying hemostatic responses, the salivary cocktail is also able to modulate the effector mechanisms of host immune responses facilitating, in turn, the transmission of several types of microorganisms. Understanding how the mosquito uses its salivary components to circumvent host immunity might help to clarify the mechanisms of transmission of such pathogens and disease establishment. Methods Flow cytometry was used to evaluate if increasing concentrations of A. aegypti salivary gland extract (SGE) affects bone marrow-derived DC differentiation and maturation. Lymphocyte proliferation in the presence of SGE was estimated by a colorimetric assay. Western blot and Annexin V staining assays were used to assess apoptosis in these cells. Naïve and memory cells from mosquito-bite exposed mice or OVA-immunized mice and their respective controls were analyzed by flow cytometry. Results Concentration-response curves were employed to evaluate A. aegypti SGE effects on DC and lymphocyte biology. DCs differentiation from bone marrow precursors, their maturation and function were not directly affected by A. aegypti SGE (concentrations ranging from 2.5 to 40 μg/mL). On the other hand, lymphocytes were very sensitive to the salivary components and died in the presence of A. aegypti SGE, even at concentrations as low as 0.1 μg/mL. In addition, A. aegypti SGE was shown to induce apoptosis in all lymphocyte populations evaluated (CD4+ and CD8+ T cells, and B cells) through a mechanism involving caspase-3 and caspase-8, but not Bim. By using different approaches to generate memory cells, we were able to verify that these cells are resistant to SGE effects. Conclusion Our results show that lymphocytes, and not DCs, are the primary target of A. aegypti salivary components. In the presence of A. aegypti SGE, naïve lymphocyte populations die by apoptosis in a caspase-3- and caspase-8-dependent pathway, while memory cells are selectively more resistant to its effects. The present work contributes to elucidate the activities of A. aegypti salivary molecules on the antigen presenting cell-lymphocyte axis and in the biology of these cells.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Limits on Spin-Dependent WIMP-Nucleon Cross Sections from 225 Live Days of XENON100 Data

We present new experimental constraints on the elastic, spin-dependent WIMP-nucleon cross section using recent data from the XENON100 experiment, operated in the Laboratori Nazionali del Gran Sasso in Italy. An analysis of 224.6 live days x 34 kg of exposure acquired during 2011 and 2012 revealed no excess signal due to axial-vector WIMP interactions with Xe-129 and Xe-131 nuclei. This leads to the most stringent upper limits on WIMP-neutron cross sections for WIMP masses above 6 GeV/c(2), with a minimum cross section of 3.5 x 10(-40) cm(2) at a WIMP mass of 45 GeV/c(2), at 90% confidence level.

On Modeling an Innovative Monitoring Network for Protecting and Managing Cultural Heritage from Risk Events

In this paper the model of an Innovative Monitoring Network involving properly connected nodes to develop an Information and Communication Technology (ICT) solution for preventive maintenance of historical centres from early warnings is proposed. It is well known that the protection of historical centres generally goes from a large-scale monitoring to a local one and it could be supported by a unique ICT solution. More in detail, the models of a virtually organized monitoring system could enable the implementation of automated analyses by presenting various alert levels. An adequate ICT solution tool would allow to define a monitoring network for a shared processing of data and results. Thus, a possible retrofit solution could be planned for pilot cases shared among the nodes of the network on the basis of a suitable procedure utilizing a retrofit catalogue. The final objective would consist in providing a model of an innovative tool to identify hazards, damages and possible retrofit solutions for historical centres, assuring an easy early warning support for stakeholders. The action could proactively target the needs and requirements of users, such as decision makers responsible for damage mitigation and safeguarding of cultural heritage assets.

System-on-a-Chip (SoC) based hardware acceleration in Register Transfer Level (RTL) design

Today, modern System-on-a-Chip (SoC) systems have grown rapidly due to the increased processing power, while maintaining the size of the hardware circuit. The number of transistors on a chip continues to increase, but current SoC designs may not be able to exploit the potential performance, especially with energy consumption and chip area becoming two major concerns. Traditional SoC designs usually separate software and hardware. Thus, the process of improving the system performance is a complicated task for both software and hardware designers. The aim of this research is to develop hardware acceleration workflow for software applications. Thus, system performance can be improved with constraints of energy consumption and on-chip resource costs. The characteristics of software applications can be identified by using profiling tools. Hardware acceleration can have significant performance improvement for highly mathematical calculations or repeated functions. The performance of SoC systems can then be improved, if the hardware acceleration method is used to accelerate the element that incurs performance overheads. The concepts mentioned in this study can be easily applied to a variety of sophisticated software applications. The contributions of SoC-based hardware acceleration in the hardware-software co-design platform include the following: (1) Software profiling methods are applied to H.264 Coder-Decoder (CODEC) core. The hotspot function of aimed application is identified by using critical attributes such as cycles per loop, loop rounds, etc. (2) Hardware acceleration method based on Field-Programmable Gate Array (FPGA) is used to resolve system bottlenecks and improve system performance. The identified hotspot function is then converted to a hardware accelerator and mapped onto the hardware platform. Two types of hardware acceleration methods – central bus design and co-processor design, are implemented for comparison in the proposed architecture. (3) System specifications, such as performance, energy consumption, and resource costs, are measured and analyzed. The trade-off of these three factors is compared and balanced. Different hardware accelerators are implemented and evaluated based on system requirements. 4) The system verification platform is designed based on Integrated Circuit (IC) workflow. Hardware optimization techniques are used for higher performance and less resource costs. Experimental results show that the proposed hardware acceleration workflow for software applications is an efficient technique. The system can reach 2.8X performance improvements and save 31.84% energy consumption by applying the Bus-IP design. The Co-processor design can have 7.9X performance and save 75.85% energy consumption.

Optimization of a multielectrode array (MEA)-based approach to study the impact of Aβ on the SH-SY5Y cell line

The human brain stores, integrates, and transmits information recurring to millions of neurons, interconnected by countless synapses. Though neurons communicate through chemical signaling, information is coded and conducted in the form of electrical signals. Neuroelectrophysiology focus on the study of this type of signaling. Both intra and extracellular approaches are used in research, but none holds as much potential in high-throughput screening and drug discovery, as extracellular recordings using multielectrode arrays (MEAs). MEAs measure neuronal activity, both in vitro and in vivo. Their key advantage is the capability to record electrical activity at multiple sites simultaneously. Alzheimer’s disease (AD) is the most common neurodegenerative disease and one of the leading causes of death worldwide. It is characterized by neurofibrillar tangles and aggregates of amyloid-β (Aβ) peptides, which lead to the loss of synapses and ultimately neuronal death. Currently, there is no cure and the drugs available can only delay its progression. In vitro MEA assays enable rapid screening of neuroprotective and neuroharming compounds. Therefore, MEA recordings are of great use in both AD basic and clinical research. The main aim of this thesis was to optimize the formation of SH-SY5Y neuronal networks on MEAs. These can be extremely useful for facilities that do not have access to primary neuronal cultures, but can also save resources and facilitate obtaining faster high-throughput results to those that do. Adhesion-mediating compounds proved to impact cell morphology, viability and exhibition of spontaneous electrical activity. Moreover, SH-SY5Y cells were successfully differentiated and demonstrated acute effects on neuronal function after Aβ addition. This effect on electrical signaling was dependent on Aβ oligomers concentration. The results here presented allow us to conclude that the SH-SY5Y cell line can be successfully differentiated in properly coated MEAs and be used for assessing acute Aβ effects on neuronal signaling.

System-on-a-Chip (SoC) based Hardware Acceleration in Register Transfer Level (RTL) Design

Today, modern System-on-a-Chip (SoC) systems have grown rapidly due to the increased processing power, while maintaining the size of the hardware circuit. The number of transistors on a chip continues to increase, but current SoC designs may not be able to exploit the potential performance, especially with energy consumption and chip area becoming two major concerns. Traditional SoC designs usually separate software and hardware. Thus, the process of improving the system performance is a complicated task for both software and hardware designers. The aim of this research is to develop hardware acceleration workflow for software applications. Thus, system performance can be improved with constraints of energy consumption and on-chip resource costs. The characteristics of software applications can be identified by using profiling tools. Hardware acceleration can have significant performance improvement for highly mathematical calculations or repeated functions. The performance of SoC systems can then be improved, if the hardware acceleration method is used to accelerate the element that incurs performance overheads. The concepts mentioned in this study can be easily applied to a variety of sophisticated software applications. The contributions of SoC-based hardware acceleration in the hardware-software co-design platform include the following: (1) Software profiling methods are applied to H.264 Coder-Decoder (CODEC) core. The hotspot function of aimed application is identified by using critical attributes such as cycles per loop, loop rounds, etc. (2) Hardware acceleration method based on Field-Programmable Gate Array (FPGA) is used to resolve system bottlenecks and improve system performance. The identified hotspot function is then converted to a hardware accelerator and mapped onto the hardware platform. Two types of hardware acceleration methods – central bus design and co-processor design, are implemented for comparison in the proposed architecture. (3) System specifications, such as performance, energy consumption, and resource costs, are measured and analyzed. The trade-off of these three factors is compared and balanced. Different hardware accelerators are implemented and evaluated based on system requirements. 4) The system verification platform is designed based on Integrated Circuit (IC) workflow. Hardware optimization techniques are used for higher performance and less resource costs. Experimental results show that the proposed hardware acceleration workflow for software applications is an efficient technique. The system can reach 2.8X performance improvements and save 31.84% energy consumption by applying the Bus-IP design. The Co-processor design can have 7.9X performance and save 75.85% energy consumption.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.

A Functional Verification Methodology Based on Parameter Domains for Efficient Input Stimuli Generation and Coverage Modeling

Modern Integrated Circuit (IC) design is characterized by a strong trend of Intellectual Property (IP) core integration into complex system-on-chip (SOC) architectures. These cores require thorough verification of their functionality to avoid erroneous behavior in the final device. Formal verification methods are capable of detecting any design bug. However, due to state explosion, their use remains limited to small circuits. Alternatively, simulation-based verification can explore hardware descriptions of any size, although the corresponding stimulus generation, as well as functional coverage definition, must be carefully planned to guarantee its efficacy. In general, static input space optimization methodologies have shown better efficiency and results than, for instance, Coverage Directed Verification (CDV) techniques, although they act on different facets of the monitored system and are not exclusive. This work presents a constrained-random simulation-based functional verification methodology where, on the basis of the Parameter Domains (PD) formalism, irrelevant and invalid test case scenarios are removed from the input space. To this purpose, a tool to automatically generate PD-based stimuli sources was developed. Additionally, we have developed a second tool to generate functional coverage models that fit exactly to the PD-based input space. Both the input stimuli and coverage model enhancements, resulted in a notable testbench efficiency increase, if compared to testbenches with traditional stimulation and coverage scenarios: 22% simulation time reduction when generating stimuli with our PD-based stimuli sources (still with a conventional coverage model), and 56% simulation time reduction when combining our stimuli sources with their corresponding, automatically generated, coverage models.

Variability on red blood cell transfusion practices among Brazilian neonatal intensive care units

BACKGROUND: Guidelines for red blood cell (RBC) transfusions exist; however, transfusion practices vary among centers. This study aimed to analyze transfusion practices and the impact of patients and institutional characteristics on the indications of RBC transfusions in preterm infants. STUDY DESIGN AND METHODS: RBC transfusion practices were investigated in a multicenter prospective cohort of preterm infants with a birth weight of less than 1500 g born at eight public university neonatal intensive care units of the Brazilian Network on Neonatal Research. Variables associated with any RBC transfusions were analyzed by logistic regression analysis. RESULTS: Of 952 very-low-birth-weight infants, 532 (55.9%) received at least one RBC transfusion. The percentages of transfused neonates were 48.9, 54.5, 56.0, 61.2, 56.3, 47.8, 75.4, and 44.7%, respectively, for Centers 1 through 8. The number of transfusions during the first 28 days of life was higher in Center 4 and 7 than in other centers. After 28 days, the number of transfusions decreased, except for Center 7. Multivariate logistic regression analysis showed higher likelihood of transfusion in infants with late onset sepsis (odds ratio [OR], 2.8; 95% confidence interval [CI], 1.8-4.4), intraventricular hemorrhage (OR, 9.4; 95% CI, 3.3-26.8), intubation at birth (OR, 1.7; 95% CI, 1.0-2.8), need for umbilical catheter (OR, 2.4; 95% CI, 1.3-4.4), days on mechanical ventilation (OR, 1.1; 95% CI, 1.0-1.2), oxygen therapy (OR, 1.1; 95% CI, 1.0-1.1), parenteral nutrition (OR, 1.1; 95% CI, 1.0-1.1), and birth center (p < 0.001). CONCLUSIONS: The need of RBC transfusions in very-low-birth-weight preterm infants was associated with clinical conditions and birth center. The distribution of the number of transfusions during hospital stay may be used as a measure of neonatal care quality.

Performance of `Tahiti` lime on twelve rootstocks under irrigated and non-irrigated conditions

Faced with new challenges, such as emerging diseases, shortening of orchard longevity, and larger social and environmental demands from consumers, practices such as rootstock diversification, irrigation and high density plantings have become relevant for the Brazilian citrus industry. This research had the objective to evaluate the performance of irrigated and non-irrigated `Tahiti` lime trees grafted on 12 rootstocks and one interstock. Plots were distributed following a randomized block design, with four replicates and one plant per plot. Rootstocks influenced plant vigor, especially `Flying Dragon` trifoliate, which reduced tree height by approximately 47% compared to the `Rangpur lime. Trees that were budded on more vigorous rootstocks showed higher yield when grown without irrigation than with irrigation. The `1646` citradia and `Morton` citrange rootstocks performed particularly well. On the other hand, the plants on less vigorous rootstocks showed better performance in terms of yield under irrigation than the same combinations without irrigation, especially those grafted on the tetraploid `Carrizo` and `Troyer` citranges, `Swingle` citrumelo, `Davis A` trifoliate and `Flying Dragon` trifoliate. Plants budded on the `1708` citradia had high yields under irrigated and non-irrigated conditions. The effect of interstock on plant vigor was dependent of rootstock. Interstocked plants on `Davis A` trifoliate were higher than those without interstock. On the other hand, interstocked plants on Catania 2 `Volkamer` lemon were less vigorous than those without interstock. (C) 2011 Elsevier B.V. All rights reserved.

Red Blood Cell Transfusions are Independently Associated with Intra-Hospital Mortality in Very Low Birth Weight Preterm Infants

Objective To test the hypothesis that red blood cell (RBC) transfusions in preterm infants are associated with increased intra-hospital mortality. Study design Variables associated with death were studied with Cox regression analysis in a prospective cohort of preterm infants with birth weight <1500 g in the Brazilian Network on Neonatal Research. Intra-hospital death and death after 28 days of life were analyzed as dependent variables. Independent variables were infant demographic and clinical characteristics and RBC transfusions. Results Of 1077 infants, 574 (53.3%) received at least one RBC transfusion during the hospital stay. The mean number of transfusions per infant was 3.3 +/- 3.4, with 2.1 +/- 2.1 in the first 28 days of life. Intra-hospital death occurred in 299 neonates (27.8%), and 60 infants (5.6%) died after 28 days of life. After adjusting for confounders, the relative risk of death during hospital stay was 1.49 in infants who received at least one RBC transfusion in the first 28 days of life, compared with infants who did not receive a transfusion. The risk of death after 28 days of life was 1.89 times higher in infants who received more than two RBC transfusions during their hospital stay, compared with infants who received one or two transfusions. Conclusion Transfusion was associated with increased death, and transfusion guidelines should consider risks and benefits of transfusion. (J Pediatr 2011; 159: 371-6).

Balance between early life tolerance and sensitization in allergy: dependence on the timing and intensity of prenatal and postnatal allergen exposure of the mother

P>Allergens can be maternally transferred to the fetus or neonate, though it is uncertain how this initial allergen exposure may impact the development of allergy responses. To evaluate the roles of timing and level of maternal allergen exposure in the early life sensitization of progeny, female BALB/c mice were given ovalbumin (OVA) orally during pregnancy, lactation or weekly at each stage to investigate the immunoglobulin E (IgE) antibody production and cellular responsiveness of their offspring. Exposure to OVA during pregnancy was also evaluated in OVA-specific T-cell receptor (TCR) transgenic (DO11.10) mice. The effect of prenatal antigen exposure on offspring sensitization was dependent on antigen intake, with low-dose OVA inducing tolerance followed by neonatal immunization that was sustained even when pups were immunized when 3 weeks old. These offspring received high levels of transforming growth factor-beta via breastfeeding. High-dose exposure during the first week of pregnancy or perinatal period induced transient inhibition of IgE production following neonatal immunization; although for later immunization IgE production was enhanced in these offspring. Postnatal maternal antigen exposure provided OVA transference via breastfeeding, which consequently induced increased offspring susceptibility to IgE antibody production according to week post-birth. The effect of low-dose maternal exposure during pregnancy was further evaluated using OVA transgenic TCR dams as a model. These progeny presented pronounced entry of CD4(+) T cells into the S phase of the cell cycle with a skewed T helper type 2 response early in life, revealing the occurrence of allergen priming in utero. The balance between tolerance and sensitization depended on the amount and timing of maternal allergen intake during pregnancy.