Penicillin vs. erythromycin in the treatment of diphtheria

PURPOSE: To review the University of Florida experience in treating ependymomas, analyze prognostic factors, and provide treatment recommendations. METHODS AND MATERIALS: Forty-one patients with ependymoma and no metastases outside the central nervous system received postoperative radiotherapy with curative intent between 1966 and 1989. Ten patients had supratentorial lesions, 22 had infratentorial lesions, and 9 had spinal cord lesions. All patients had surgery (stereotactic biopsy, subtotal resection, or gross total resection). Most patients with high-grade lesions received radiotherapy to the craniospinal axis. Low-grade intracranial lesions received more limited treatment. Spinal cord lesions were treated using either partial spine or whole spine fields. RESULTS: Of 32 intracranial tumors, 21 recurred, all at the primary site; no spinal cord tumors recurred. Overall 10-year survival rates were 51% (absolute) and 46% (relapse-free); by tumor site: spinal cord, 100%; infratentorial, 45%; supratentorial, 20% (p = 0.002). On multivariate analysis, tumor site was the only factor that influenced absolute survival (p = 0.0004); other factors evaluated included grade, gender, age, duration of symptoms, resection extent, primary tumor dose, treatment field extent, surgery-to-radiotherapy interval, and days under radiotherapy treatment. CONCLUSIONS: Patients with supratentorial or infratentorial tumors receive irradiation, regardless of grade. Craniospinal-axis fields are used when spinal seeding is radiographically or pathologically evident. Spinal cord tumors are treated using localized fields to the primary site if not completely resected. Failure to control disease at the primary site remains the main impediment to cure.     

The quantum dynamic capacity formula of a quantum channel

The dynamic capacity theorem characterizes the reliable communication rates of a quantum channel when combined with the noiseless resources of classical communication, quantum communication, and entanglement. In prior work, we proved the converse part of this theorem by making contact with many previous results in the quantum Shannon theory literature. In this work, we prove the theorem with an ??ab initio?? approach, using only the most basic tools in the quantum information theorist??s toolkit: the Alicki-Fannes?? inequality, the chain rule for quantum mutual information, elementary properties of quantum entropy, and the quantum data processing inequality. The result is a simplified proof of the theorem that should be more accessible to those unfamiliar with the quantum Shannon theory literature. We also demonstrate that the ??quantum dynamic capacity formula?? characterizes the Pareto optimal trade-off surface for the full dynamic capacity region. Additivity of this formula reduces the computation of the trade-off surface to a tractable, textbook problem in Pareto trade-off analysis, and we prove that its additivity holds for the quantum Hadamard channels and the quantum erasure channel. We then determine exact expressions for and plot the dynamic capacity region of the quantum dephasing channel, an example from the Hadamard class, and the quantum erasure channel.     

Epigallocatechin-3-Gallate-Loaded Liposomes Favor Anti-Inflammation of Microglia Cells and Promote Neuroprotection

Microglia-mediated neuroinflammation is recognized to mainly contribute to the progression of neurodegenerative diseases. Epigallocatechin-3-gallate (EGCG), known as a natural antioxidant in green tea, can inhibit microglia-mediated inflammation and protect neurons but has disadvantages such as high instability and low bioavailability. We developed an EGCG liposomal formulation to improve its bioavailability and evaluated the neuroprotective activity in in vitro and in vivo neuroinflammation models. EGCG-loaded liposomes have been prepared from phosphatidylcholine (PC) or phosphatidylserine (PS) coated with or without vitamin E (VE) by hydration and membrane extrusion method. The anti-inflammatory effect has been evaluated against lipopolysaccharide (LPS)-induced BV-2 microglial cells activation and the inflammation in the substantia nigra of Sprague Dawley rats. In the cellular inflammation model, murine BV-2 microglial cells changed their morphology from normal spheroid to activated spindle shape after 24 h of induction of LPS. In the in vitro free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, EGCG scavenged 80% of DPPH within 3 min. EGCG-loaded liposomes could be phagocytized by BV-2 cells after 1 h of cell culture from cell uptake experiments. EGCG-loaded liposomes improved the production of BV-2 microglia-derived nitric oxide and TNF-α following LPS. In the in vivo Parkinsonian syndrome rat model, simultaneous intra-nigral injection of EGCG-loaded liposomes attenuated LPS-induced pro-inflammatory cytokines and restored motor impairment. We demonstrated that EGCG-loaded liposomes exert a neuroprotective effect by modulating microglia activation. EGCG extracted from green tea and loaded liposomes could be a valuable candidate for disease-modifying therapy for Parkinson’s disease (PD).     

Reverse osmosis separations of polyethylene glycols in dilute aqueous solutions using porous cellulose acetate membranes

Reverse osmosis separations of eight polyethylene glycol (PEG) solutes in the average molecular weight range of 200 to 6750 in single-solute dilute aqueous solutions have been studied using porous cellulose acetate membranes at the operating pressures of 50, 75, and 100 psig. Diffusivity data for the above PEG solutes have also been obtained from experimental data on intrinsic viscosities. From an analysis of all experimental data, numerical values for the parameters representing the polar (?ΔΔG/RT), steric (δ^*ΣE_s), and nonpolar (ω^*Σs^*) forces governing reverse osmosis separations of PEG solutes have been generated. These numerical values are useful for precise characterization of cellulose acetate membranes for whose specifications sodium chloride is not the appropriate reference solute because of its low or practically negligible separation under reverse osmosis operating conditions. This work also illustrates that solute separation in reverse osmosis can predictably increase or decrease with increase in operating pressure depending on experimental conditions.     

Parallel matrix factorization for recommender systems

Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two popular approaches to compute matrix factorization, and there has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been thoroughly explored. In this paper, we show that coordinate descent-based methods have a more efficient update rule compared to ALS and have faster and more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updates rank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, with a synthetic dataset containing 14.6 billion ratings, on a distributed memory cluster with 64 processors, to deliver the desired test RMSE, CCD++ is 49 times faster than SGD and 20 times faster than ALS. When the number of processors is increased to 256, CCD++ takes only 16 s and is still 40 times faster than SGD and 20 times faster than ALS.     

Shrinkage and warpage prediction of injection‐molded thin‐wall parts using artificial neural networks

This study demonstrates the successful use of back‐propagation artificial neural networks (BPANNs) in predicting the shrinkage and warpage of injection‐molded thin‐wall parts. The effects of structural parameters of a BPANN on the predictionaccuracy and the capability of a BPANN in determining the optimal process condition are also discussed. The training and testing data are obtained experimentally based on a Taguchi L₂₇ (3¹³) test schedule. The results show that the trained BPANN can successfully predict the shrinkage and warpage of injection‐molded thin‐wall parts. Comparing the prediction accuracies of the trained BPANN and C‐Mold software, it is noted that the trained BPANN predicts more accurately. In terms of determining the optimal process condition for minimizing the shrinkage and warpage of injected thin‐wall parts, the trained BPANN is also shown to give a better optimal process condition than Taguchi's method. Polym. Eng. Sci. 44:2029–2040, 2004. © 2004 Society of Plastics Engineers.     

Optimal process conditions of shrinkage and warpage of thin‐wall parts

Optimal process conditions of thin‐wall injection molding of a cellular phone cover were investigated with the consideration of interaction effects between process parameters. L₂₇ experimental tests based on Taguchi's method were performed, and then Cyclone Scanner, PolyCAD and PolyWorks were used to measure the shrinkage and warpage of the thin‐wall injected parts to determine the optimal process conditions. Based on the results of the analysis of variables and the F‐test, interaction effects for each observed factor were determined. The results indicated that the packing pressure was the most important process parameter affecting the shrinkage and warpage of the thin‐wall part. The optimal process conditions were different for the shrinkage and the warpage. This was because during the injection process, the mechanisms affecting shrinkage or warpage were different. Compared with the results obtained with simplified thin‐wall parts in the literature, it was found that the geometry of a real commercial part did affect the optimal process conditions and the order of influence of process parameters. The optimal process conditions determined by Taguchi's method for reducing the shrinkage and warpage were verified experimentally in this work. Polym. Eng. Sci. 44:917–928, 2004. © 2004 Society of Plastics Engineers.