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Modern drug delivery technology began in 1952 with the advent of the Spansule® sustained-release capsule technology, which can deliver a drug for 12 h after oral administration through an initial immediate dose followed by the remaining released gradually. Until the 1980s, oral and transdermal formulations providing therapeutic durations up to 24 h for small molecules dominated the drug delivery field and the market. The introduction of Lupron Depot® in 1989 opened the door for long-acting injectables and implantables, extending the drug delivery duration from days to months and occasionally years. Notably, the new technologies allowed long-term delivery of peptide and protein drugs, although limited to parenteral administration. The introduction of the first PEGylated protein, Adagen®, in 1990 marked the new era of PEGylation, resulting in Doxil® (doxorubicin in PEGylated liposome) in 1995, Movantik® (PEGylated naloxone - naloxegol) in 2014, and Onpattro® (Patisiran - siRNA in PEGylated lipid nanoparticle) in 2018. Drug-polymer complexes were introduced, e.g., InFed® (iron-dextran complex injection) in 1974 and Abraxane® (paclitaxel-albumin complex) in 2005. In 2000, both Mylotarg™ (antibody-drug conjugate - gemtuzumab ozogamicin) and Rapamune® (sirolimus nanocrystal formulation) were introduced. The year 2000 also marked the launching of the National Nanotechnology Initiative by the U.S. government, which was soon followed by the rest of the world. Extensive work on nanomedicine, particularly formulations designed to escape from endosomes after being taken by tumor cells, along with PEGylation technology, ultimately resulted in the timely development of lipid nanoparticle formulations for COVID-19 vaccine delivery in 2020. While the advances in drug delivery technologies for the last seven decades are breathtaking, they are only the tip of an iceberg of technologies that have yet to be utilized in an approved formulation or even to be discovered. As life expectancy continues to increase, more people require long-term care for various diseases. Filling the current and future unmet needs requires innovative drug delivery technologies to overcome age-old familiar hurdles, e.g., improving water-solubility of poorly soluble drugs, overcoming biological barriers, and developing more efficient long-acting depot formulations. The lessons learned from the past are essential assets for developing future drug delivery technologies implemented into products. As the development of COVID-19 vaccines demonstrated, meeting the unforeseen crisis of the uncertain future requires continuous cumulation of failures (as learning experiences), knowledge, and technologies. Conscious efforts of supporting diversified research topics in the drug delivery field are urgently needed more than ever.

Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering methods. In contrast, for flow-based clusterings the edges between clusters tend to be oriented in one direction and have been found in migration data, food webs, and trade data. In this paper we introduce a spectral algorithm for finding flow-based clusterings. The proposed algorithm is based on recent work which uses complex-valued Hermitian matrices to represent digraphs. By establishing an algebraic relationship between a complex-valued Hermitian representation and an associated real-valued, skew-symmetric matrix the proposed algorithm produces clusterings while remaining completely in the real field. Our algorithm uses less memory and asymptotically less computation while provably preserving solution quality. We also show the algorithm can be easily implemented using standard computational building blocks, possesses better numerical properties, and loans itself to a natural interpretation via an objective function relaxation argument., Comment: 21 pages, 7 figures

We consider the problem of low-rank approximation of massive dense non-negative tensor data, for example to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory. We propose a distributed-memory parallel computing solution to handle massive data sets, loading the input data across the memories of multiple nodes and performing efficient and scalable parallel algorithms to compute the low-rank approximation. We present a software package called PLANC (Parallel Low Rank Approximation with Non-negativity Constraints), which implements our solution and allows for extension in terms of data (dense or sparse, matrices or tensors of any order), algorithm (e.g., from multiplicative updating techniques to alternating direction method of multipliers), and architecture (we exploit GPUs to accelerate the computation in this work).We describe our parallel distributions and algorithms, which are careful to avoid unnecessary communication and computation, show how to extend the software to include new algorithms and/or constraints, and report efficiency and scalability results for both synthetic and real-world data sets., Comment: arXiv admin note: text overlap with arXiv:1806.07985

One of the key advantages of visual analytics is its capability to leverage both humans's visual perception and the power of computing. A big obstacle in integrating machine learning with visual analytics is its high computing cost. To tackle this problem, this paper presents PIVE (Per-Iteration Visualization Environment) that supports real-time interactive visualization with machine learning. By immediately visualizing the intermediate results from algorithm iterations, PIVE enables users to quickly grasp insights and interact with the intermediate output, which then affects subsequent algorithm iterations. In addition, we propose a widely-applicable interaction methodology that allows efficient incorporation of user feedback into virtually any iterative computational method without introducing additional computational cost. We demonstrate the application of PIVE for various dimension reduction algorithms such as multidimensional scaling and t-SNE and clustering and topic modeling algorithms such as k-means and latent Dirichlet allocation.

The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero. In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data. In this paper, we present a matrix factorization method, which is naturally able to distinguish the anomalies with the use of low rank approximations of the underlying data. Our iterative algorithm TONMF is based on block coordinate descent (BCD) framework. We define blocks over the term-document matrix such that the function becomes solvable. Given most recently updated values of other matrix blocks, we always update one block at a time to its optimal. Our approach has significant advantages over traditional methods for text outlier detection. Finally, we present experimental results illustrating the effectiveness of our method over competing methods., Comment: Accepted at 2017 SIAM Data Mining Conference

UnlabelledCellular immunity is pivotal in HIV-1 pathogenesis but is hampered by viral sequence diversity. An approach to minimize this diversity is to focus immunity on conserved proteome sequences; therefore, we selected four relatively conserved regions (Gag amino acids 148 to 214 and 250 to 335, Env amino acids 521 to 606, and Nef amino acids 106 to 148), each created in three mosaics, to provide better coverage of M-group HIV-1 sequences. A conserved-region vaccine (CRV) delivering genes for these four regions as equal mixtures of three mosaics each (each region at a separate injection site) was compared to a whole-protein vaccine (WPV) delivering equimolar amounts of genes for whole Gag, Env, and Nef as clade B consensus sequences (separate injection sites). Three rhesus macaques were vaccinated via three DNA primes and a recombinant adenovirus type 5 boost (weeks 0, 4, 8, and 24, respectively). Although CRV inserts were about one-fifth that of WPV, the CRV generated comparable-magnitude blood CD4+ and CD8+ T lymphocyte responses against Gag, Env, and Nef. WPV responses preferentially targeted proteome areas outside the selected conserved regions in direct proportion to sequence lengths, indicating similar immunogenicities for the conserved regions and the outside regions. The CRV yielded a conserved-region targeting density that was approximately 5-fold higher than that of the WPV. A similar pattern was seen for bronchoalveolar lymphocytes, but with quadruple the magnitudes seen in blood. Overall, these findings demonstrate that the selected conserved regions are highly immunogenic and that anatomically isolated vaccinations with these regions focus immunodominance compared to the case for full-length protein vaccination.ImportanceHIV-1 sequence diversity is a major barrier limiting the capability of cellular immunity to contain infection and the ability of vaccines to match circulating viral sequences. To date, vaccines tested in humans have delivered whole proteins or genes for whole proteins, and it is unclear whether including only conserved sequences would yield sufficient cellular immunogenicity. We tested a vaccine delivering genes for four small conserved HIV-1 regions compared to a control vaccine with genes for whole Gag, Env, and Nef. Although the conserved regions ranged from 43 to 86 amino acids and comprised less than one-fifth of the whole Gag/Env/Nef sequence, the vaccines elicited equivalent total magnitudes of both CD4+ and CD8+ T lymphocyte responses. These data demonstrate the immunogenicity of these small conserved regions and the potential for a vaccine to steer immunodominance toward conserved epitopes.

Integer data sets frequently appear in many applications in sciences and technology. To analyze these, integer low rank approximation has received much attention due to its capacity of representing the results in integers preserving the meaning of the original data sets. To our knowledge, none of previously proposed techniques developed for real numbers can be successfully applied, since integers are discrete in nature. In this work, we start with a thorough review of algorithms for solving integer least squares problems, {and} then develop a block coordinate descent method based on the integer least squares estimation to obtain the integer low rank approximation of integer matrices. The numerical application on association analysis and numerical experiments on random integer matrices are presented. Our computed results seem to suggest that our method can find a more accurate solution than other existing methods for continuous data sets., Comment: 18 pages, 2 figures