Serologic evidence of previous Campylobacter jejuni infection in patients with the Guillain-Barré syndrome

OBJECTIVE: To determine if patients with the Guillain-Barré syndrome are likely to have had Campylobacter jejuni infection before onset of neurologic symptoms. DESIGN: A case-control study. SETTING: Several university medical centers. PATIENTS: Case patients met clinical criteria for the Guillain-Barré syndrome between 1983 and 1990 and had a serum sample collected and frozen within 3 weeks after onset of neurologic symptoms (n = 118). Disease controls were patients with other neurologic illnesses (n = 56); healthy controls were hospital employees or healthy family members of patients (n = 47). MEASUREMENTS: Serum IgA, IgG, and IgM antibodies to C. jejuni were determined by enzyme-linked immunosorbent assays. Assays were done in a blinded manner. RESULTS: Optical density ratios > or = 2 in two or more immunoglobulin classes were seen in 43 (36%) of patients with the Guillain-Barré syndrome and in 10 (10%) of controls (odds ratio, 5.3; 95% CI, 2.4 to 12.5; P < 0.001). Increasing the optical density ratio or the number of immunoglobulin classes necessary to yield a positive result increased the strength of the association. The number of patients with the Guillain-Barré syndrome who had positive serologic responses was greatest from September to November (P = 0.02). Male patients were three times more likely to have serologic evidence of C. jejuni infection (P = 0.009); the proportion of patients with the syndrome who had a positive serologic response increased with age. CONCLUSIONS: Patients with the Guillain-Barré syndrome are more likely than controls to have serologic evidence of C. jejuni infection in the weeks before onset of neurologic symptoms. Campylobacter jejuni may play a role in the initiation of the Guillain-Barré syndrome in many patients.     

The effect of fibre orientation of the dry sliding wear of borsic-reinforced 2014 aluminium alloy

The effect of fibre orientation on the dry sliding wear of continuous B(SiC) fibre reinforced aluminium alloy composites was investigated using a pin-on-disc wear testing machine. The metal-matrix composites (MMC) samples were tested in the normal (N), parallel (P) and antiparallel (AP) orientations sliding against a steel counter disc at a fixed speed of 1 m s^–1 under loads of from 12 to 60 N.The results showed that for the matrix alloy and MMCs, the average wear increased linearly with load. Wear of the MMCs was insensitive to fibre content but for composites with fibre contents at or above the minimum of 16 vol% used for this work, the wear rate was about 18% of that of the unreinforced matrix. Fibre orientation had a minor effect on wear rate; the N orientation gave the lowest wear rate with the AP orientation slightly higher and the P orientation significantly higher.The average coefficients of friction of the MMCs in N and AP orientations decreased linearly with increased wear rate and non-linearly with increased load, but the P orientation was insensitive to either variable.It was concluded from these results and a metallographic examination that the mechanism of wear of MMCs was essentially oxidative wear of the matrix. The hard fibres modified this to slightly different degrees depending on their orientation relative to the wear surface and sliding direction.     

Thermodynamic analysis of solar photovoltaic cell systems

The thermodynamic characteristics of solar photovoltaic (PV) cells are investigated from a perspective based on exergy. A new efficiency is developed that is useful in studying PV performance and possible improvements. Exergy analysis is applied to a PV system and its components, and exergy flows, losses and efficiencies are evaluated. Energy efficiency is seen to vary between 7% and 12% during the day. In contrast, exergy efficiencies, which incorporate the second law of thermodynamics and account for solar irradiation exergy values, are lower for electricity generation using the considered PV system, ranging from 2% to 8%. Values of “fill factors” are determined for the system and observed to be similar to values of exergy efficiency.     

Oxidation behavior of single-crystal Al2O3-fiber-reinforced Ni3Al-based composites

A series of single-crystal Al₂O₃-fiber-reinforced Ni3Al-based intermetallic matrix composites were fabricated by pressure casting. The matrices employed were binary Ni₃Al, Ni₃Al-0.5 at. pct Cr, and Ni₃Al-0.34 at. pct Zr. The development of microstructure upon oxidation in air at either 1100 °C or 1200 °C was investigated by optical, scanning, and transmission electron microscopy. In air-oxidized binary Ni₃Al, some of the fibers were fully or partially covered with a layer of oxide. A weak fiber/matrix bond in this system, which led to fiber debonding during composite processing, is believed to be responsible for the ingress of O into the composite and oxidation of the matrix in the debonded regions at the fiber/matrix interface. Addition of Cr to Ni₃Al resulted in an almost threefold increase in fiber/matrix bond strength. No oxidation of the interface was observed. A thick layer of oxide was formed around all the fibers when the composite was thermally cycled prior to isothermal annealing. Addition of Zr to Ni₃Al resulted in the formation of a layer of ZrO₂ on the surface of the fibers during composite processing. The ZrO₂ layer provided a fast path for the diffusion of O, which led to the formation of a rootlike oxide structure around the fibers. The rootlike structure consisted of a network of Al₂O₃-covered ZrO₂.     

The IFR/DFR property of the forward recurrence-time distribution

When a failure or replacement process is modeled as a renewal process, the residual life of the unit in use at a given time is generally referred to as the forward recurrence time (RT). Distributional properties of this random variable are critically important in many applications. This paper investigates the extent to which the failure-rate function monotonicity of a life distribution is inherited by the forward RT distribution at time s of its renewal process. For DFR life distributions, the forward RT distribution is also DFR for every s⩾0. However, the corresponding property does not necessarily hold for IFR life distributions. The forward RT distribution is IFR in the limit as s→0 and as s→∞. For IFR Weibull life distributions, we demonstrate numerically that the forward RT distribution is IFR for small s. As s is increased, it alternates between being IFR and non-IFR in an interesting cyclical pattern, and remains IFR beyond a large enough s     

Film morphology and reaction rate for the CVD of tungsten by the WF6—SiH4 reaction

Journal of Electronic Materials - Low pressure chemical vapor deposition (LPCVD) of tungsten (W) by SiH4 reduction of WF6 on Si(100) surfaces was studied in a single-wafer, cold-wall reactor over a...     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Salt and pepper noise filtering with fuzzy-cellular automata

A new image denoising algorithm is proposed to restore digital images corrupted by impulse noise. It is based on two dimensional cellular automata (CA) with the help of fuzzy logic theory. The algorithm describes a local fuzzy transition rule which gives a membership value to the corrupted pixel neighborhood and assigns next state value as a central pixel value. The proposed method removes the noise effectively even at noise level as high as 90%. Extensive simulations show that the proposed algorithm provides better performance than many of the existing filters in terms of noise suppression and detail preservation. Also, qualitative and quantitative measures of the image produce better results on different images compared with the other algorithms.