DeepHP: A New Gastric Mucosa Histopathology Dataset for Helicobacter pylori Infection Diagnosis

Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.     

An experimental evaluation of cross-layer routing in a wireless mesh backbone

Over the latest few years, cross-layer design in wireless networks has drawn great attention from the research community. One of the main arguments in favor of such techniques is that the hop-count metric alone is not enough to capture the specificities of wireless links (e.g., interferences, collisions, fading). In this paper, we address a simple yet fundamental question: What are the real improvements that cross-layering can bring to routing performance when compared to the simple hop-count metric? In our experiments, we consider the backbone of a real wireless mesh network composed of 12 routers deployed in an office building. We focus on the stability of routes and their persistence. In spite of the nature of cross-layer metrics that take into account information from different layers, lets them be very reactive to changes, we observe that using these metrics, pairs of nodes tend to mainly use the same set of two or three routes between them.     

Damage Model and Progressive Failure Analyses for Filament Wound Composite Laminates

Recent improvements in manufacturing processes and materials properties associated with excellent mechanical characteristics and low weight have made composite materials very attractive for application on civil aircraft structures. However, even new designs are still very conservative, because the composite failure phenomenon is very complex. Several failure criteria and theories have been developed to describe the damage process and how it evolves, but the solution of the problem is still open. Moreover, modern filament winding techniques have been used to produce a wide variety of structural shapes not only cylindrical parts, but also “flat” laminates. Therefore, this work presents the development of a damage model and its application to simulate the progressive failure of flat composite laminates made using a filament winding process. The damage model was implemented as a UMAT (User Material Subroutine), in ABAQUS^TM Finite Element (FE) framework. Progressive failure analyses were carried out using FE simulation in order to simulate the failure of flat filament wound composite structures under different loading conditions. In addition, experimental tests were performed in order to identify parameters related to the material model, as well as to evaluate both the potential and the limitations of the model. The difference between numerical and the average experimental results in a four point bending set-up is only 1.6 % at maximum load amplitude. Another important issue is that the model parameters are not so complicated to be identified. This characteristic makes this model very attractive to be applied in an industrial environment.     

Molds and Resists Studies for Nanoimprint Lithography of Electrodes in Low-Voltage Polymer Thin-Film Transistors

A low-cost patterning of electrodes was investigated looking forward to replacing conventional photolithography for the processing of low-operating voltage polymeric thin-film transistors. Hard silicon, etched by sulfur hexafluoride and oxygen gas mixture, and flexible polydimethylsiloxane imprinting molds were studied through atomic force microscopy (AFM) and field emission gun scanning electron microscopy. The higher the concentration of oxygen in reactive ion etching, the lower the etch rate, sidewall angle, and surface roughness. A concentration around 30 % at 100 mTorr, 65 W and 70 sccm was demonstrated as adequate for submicrometric channels, presenting a reduced etch rate of 176 nm/min. Imprinting with positive photoresist AZ1518 was compared to negative SU-8 2002 by optical microscopy and AFM. Conformal results were obtained only with the last resist by hot embossing at 120 °C and 1 kgf/cm² for 2 min, followed by a 10 min post-baking at 100 °C. The patterning procedure was applied to define gold source and drain electrodes on oxide-covered substrates to produce bottom-gate bottom-contact transistors. Poly(3-hexylthiophene) (P3HT) devices were processed on high-κ titanium oxynitride (TiO _x N _y ) deposited by radiofrequency magnetron sputtering over indium tin oxide-covered glass to achieve low-voltage operation. Hole mobility on micrometric imprinted channels may approach amorphous silicon (～0.01 cm²/V s) and, since these devices operated at less than 5 V, they are not only suitable for electronic applications but also as sensors in aqueous media.     

Effects of high‐pressure homogenisation on physicochemical characteristics of partially skimmed milk

The changes in partially skimmed milk (0.5% fat) physicochemical properties and proteins after high‐pressure homogenisation (HPH) at 100, 200 and 300 MPa were investigated. Processing parameters and changes in pH, ethanol precipitation stability, lightness, whey protein denaturation, hydrophobicity and viscosity were evaluated. No significant differences were found between milk pH and nonprotein nitrogen content before and after HPH. Ethanol stability, lightness and hydrophobicity increased when pressure was increased from 100 MPa to 300 MPa. Whey protein denaturation, evaluated through noncasein nitrogen, occurred only at 200 to 300 MPa, and viscosity increased just at 300 MPa. Therefore, HPH changed some milk physicochemical characteristics, mainly those related to protein content. These results highlight that HPH processing is a promising technology to improve partially skimmed milk mouth feel being suitable for dairy products manufacturing.     

Relationships between MODIS phenological metrics,topographic shade,and anomalous temperature patterns in seasonal deciduous forests of south Brazil

We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.     

Adaptive Boolean Logic Using Ferroelectrics Capacitors as Basic Units of Artificial Neurons and Its Implementation in FPGA

In this work we propose the implementation of boolean logic through artificial neurons with Ferroelectric Capacitor (FeCapacitor) as its basic unit on a reconfigurable hardware platform. Two neurons were implemented: the Perceptron and the Spiking Neuron model. Both neurons use the phenomenon of the hysteresis loop as an activation function and were embedded on a Field Gate Programmable Gate Array (FPGA) hardware platform. The implementations were carried out by Simulink models and hardware synthesizable blocks from DSP Builder software and the results are shown in the form of the models and the boolean functions implemented by them.