Biowaiver monographs for immediate release solid oral dosage forms: Doxycycline hyclate

Literature data relevant to the decision to allow a waiver of in vivo bioequivalence (BE) testing for the approval of immediate release (IR) solid oral dosage forms containing doxycycline hyclate are reviewed. According to the Biopharmaceutics Classification System (BCS), doxycycline hyclate can be assigned to BCS Class I. No problems with BE of IR doxycycline formulations containing different excipients and produced by different manufacturing methods have been reported and hence the risk of bio in equivalence caused by these factors appears to be low. Doxycycline has a wide therapeutic index. Further, BCS-based dissolution methods have been shown to be capable of identifying formulations which may dissolve too slowly to generate therapeutic levels. It is concluded that a biowaiver is appropriate for IR solid oral dosage forms containing doxycycline hyclate as the single Active Pharmaceutical Ingredient (API) provided that (a) the test product contains only excipients present in doxycycline hyclate IR solid oral drug products approved in the International Conference on Harmonization (ICH) or associated countries; and (b) the comparator and the test products comply with the BCS criteria for “very rapidly dissolving” or, alternatively, when similarity of the dissolution profiles can be demonstrated and the two products are “rapidly dissolving.”. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99: 1639–1653, 2010Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64911/1/21954_ftp.pd

Deep Networks for Compressed Image Sensing

The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been recently proposed and obtained superior performance. However, there still exist two important challenges within the CS theory. The first one is how to design a sampling mechanism to achieve an optimal sampling efficiency, and the second one is how to perform the reconstruction to get the highest quality to achieve an optimal signal recovery. In this paper, we try to deal with these two problems with a deep network. First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process. Then, we propose a deep network to recover the image, which imitates traditional compressed sensing reconstruction processes. Experimental results demonstrate that our deep networks based CS reconstruction method offers a very significant quality improvement compared against state of the art ones.Comment: This paper has been accepted by the IEEE International Conference on Multimedia and Expo (ICME) 201

Efficient Compression Technique for Sparse Sets

Recent technological advancements have led to the generation of huge amounts of data over the web, such as text, image, audio and video. Most of this data is high dimensional and sparse, for e.g., the bag-of-words representation used for representing text. Often, an efficient search for similar data points needs to be performed in many applications like clustering, nearest neighbour search, ranking and indexing. Even though there have been significant increases in computational power, a simple brute-force similarity-search on such datasets is inefficient and at times impossible. Thus, it is desirable to get a compressed representation which preserves the similarity between data points. In this work, we consider the data points as sets and use Jaccard similarity as the similarity measure. Compression techniques are generally evaluated on the following parameters --1) Randomness required for compression, 2) Time required for compression, 3) Dimension of the data after compression, and 4) Space required to store the compressed data. Ideally, the compressed representation of the data should be such, that the similarity between each pair of data points is preserved, while keeping the time and the randomness required for compression as low as possible. We show that the compression technique suggested by Pratap and Kulkarni also works well for Jaccard similarity. We present a theoretical proof of the same and complement it with rigorous experimentations on synthetic as well as real-world datasets. We also compare our results with the state-of-the-art "min-wise independent permutation", and show that our compression algorithm achieves almost equal accuracy while significantly reducing the compression time and the randomness

Dynamic structure function of a cold Fermi gas at unitarity

We present a theoretical study of the dynamic structure function of a resonantly interacting two-component Fermi gas at zero temperature. Our approach is based on dynamic many-body theory able to describe excitations in strongly correlated Fermi systems. The fixed-node diffusion Monte Carlo method is used to produce the ground-state correlation functions which are used as an input for the excitation theory. Our approach reproduces recent Bragg scattering data in both the density and the spin channel. In the BCS regime, the response is close to that of the ideal Fermi gas. On the BEC side, the Bose peak associated with the formation of dimers dominates the density channel of the dynamic response. When the fraction of dimers is large our theory departs from the experimental data, mainly in the spin channel.Peer ReviewedPostprint (published version