Indefinite Knapsack Separable Quadratic Programming: Methods and Applications

Quadratic programming (QP) has received significant consideration due to an extensive list of applications. Although polynomial time algorithms for the convex case have been developed, the solution of large scale QPs is challenging due to the computer memory and speed limitations. Moreover, if the QP is nonconvex or includes integer variables, the problem is NP-hard. Therefore, no known algorithm can solve such QPs efficiently. Alternatively, row-aggregation and diagonalization techniques have been developed to solve QP by a sub-problem, knapsack separable QP (KSQP), which has a separable objective function and is constrained by a single knapsack linear constraint and box constraints. KSQP can therefore be considered as a fundamental building-block to solve the general QP and is an important class of problems for research. For the convex KSQP, linear time algorithms are available. However, if some quadratic terms or even only one term is negative in KSQP, the problem is known to be NP-hard, i.e. it is notoriously difficult to solve. The main objective of this dissertation is to develop efficient algorithms to solve general KSQP. Thus, the contributions of this dissertation are five-fold. First, this dissertation includes comprehensive literature review for convex and nonconvex KSQP by considering their computational efficiencies and theoretical complexities. Second, a new algorithm with quadratic time worst-case complexity is developed to globally solve the nonconvex KSQP, having open box constraints. Third, the latter global solver is utilized to develop a new bounding algorithm for general KSQP. Fourth, another new algorithm is developed to find a bound for general KSQP in linear time complexity. Fifth, a list of comprehensive applications for convex KSQP is introduced, and direct applications of indefinite KSQP are described and tested with our newly developed methods. Experiments are conducted to compare the performance of the developed algorithms with that of local, global, and commercial solvers such as IBM CPLEX using randomly generated problems in the context of certain applications. The experimental results show that our proposed methods are superior in speed as well as in the quality of solutions

Genome-Scale Promoter Engineering by Coselection MAGE

Multiplex Automated Genome Engineering (MAGE) employs short oligonucleotides to scarlessly modify genomes. However, insertions of >10 bases are still inefficient, but can be improved substantially by selection of highly modified chromosomes. Here, we describe Co-Selection MAGE (CoS-MAGE) to optimize biosynthesis of aromatic amino acid derivatives by combinatorially inserting multiple T7 promoters simultaneously into 12 genomic operons. Promoter libraries can be quickly generated to study gain-of-function epistatic interactions in gene networks

MiR-135-5p-p62 Axis Regulates Autophagic Flux, Tumorigenic Potential, and Cellular Interactions Mediated by Extracellular Vesicles During Allergic Inflammation

The objective of this study was to investigate the relationship between autophagy and allergic inflammation. In vitro allergic inflammation was accompanied by an increased autophagic flux in rat basophilic leukemia (RBL2H3) cells. 3-MA, an inhibitor of autophagic processes, negatively regulated allergic inflammation both in vitro and in vivo. The role of p62, a selective receptor of autophagy, in allergic inflammation was investigated. P62, increased by antigen stimulation, mediated in vitro allergic inflammation, passive cutaneous anaphylaxis (PCA), and passive systemic anaphylaxis (PSA). P62 mediated cellular interactions during allergic inflammation. It also mediated tumorigenic and metastatic potential of cancer cells enhanced by PSA. TargetScan analysis predicted that miR-135-5p was a negative regulator of p62. Luciferase activity assay showed that miR-135-5p directly regulated p62. MiR-135-5p mimic negatively regulated features of allergic inflammation and inhibited tumorigenic and metastatic potential of cancer cells enhanced by PSA. MiR-135-5p mimic also inhibited cellular interactions during allergic inflammation. Extracellular vesicles mediated allergic inflammation both in vitro and in vivo. Extracellular vesicles were also necessary for cellular interactions during allergic inflammation. Transmission electron microscopy showed p62 within extracellular vesicles of antigen-stimulated rat basophilic leukemia cells (RBL2H3). Extracellular vesicles isolated from antigen-stimulated RBL2H3 cells induced activation of macrophages and enhanced invasion and migration potential of B16F1 mouse melanoma cells in a p62-dependent manner. Extracellular vesicles isolated from PSA-activated BALB/C mouse enhanced invasion and migration potential of B16F1 cells, and induced features of allergic inflammation in RBL2H3 cells. Thus, miR-135-5p-p62 axis might serve as a target for developing anti-allergy drugs

Detection of Tomato Leaf Miner Using Deep Neural Network

As a result of climate change and global warming, plant diseases and pests are drawing attention because they are dispersing more quickly than ever before. The tomato leaf miner destroys the growth structure of the tomato, resulting in 80 to 100 percent tomato loss. Despite extensive efforts to prevent its spread, the tomato leaf miner can be found on most continents. To protect tomatoes from the tomato leaf miner, inspections must be performed on a regular basis throughout the tomato life cycle. To find a better deep neural network (DNN) approach for detecting tomato leaf miner, we investigated two DNN models for classification and segmentation. The same RGB images of tomato leaves captured from real-world agricultural sites were used to train the two DNN models. Precision, recall, and F1-score were used to compare the performance of two DNN models. In terms of diagnosing the tomato leaf miner, the DNN model for segmentation outperformed the DNN model for classification, with higher precision, recall, and F1-score values. Furthermore, there were no false negative cases in the prediction of the DNN model for segmentation, indicating that it is adequate for detecting plant diseases and pests

Mathematical Modeling and Analysis of Chemotherapy Strategy in the Treatment of HIV

The World Health Organization has reported that Acquired Immune Deficiency Syndrome (AIDS) has been found to be the second leading cause of death by diseases. Therefore, the importance of treating the disease has been greatly emphasized. These days, scientists have suggested that chemotherapy could be the most effective way of treating AIDS. We built a mathematical model of dynamics of the Human Immunodeficiency Virus (HIV) through a system of differential equations, which describes the interactions between the HIV and T-cells that are immune cells attacked by the HIV. The model produced the concentration rate of infected and uninfected T-cells over time when there is a medical intervention, called chemotherapy. We mathematically analyzed the dynamics of the immune system and the HIV and performed the computer simulations for various chemotherapy strategies

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads' (RRHs') energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations. © IEEE.FALS

Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems

In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases. This paper presents a communication data optimization scheme to mitigate the decrease in throughput due to communication performance bottlenecks in distributed deep learning. To optimize communication, we propose two methods. The first is a layer dropping scheme to reduce communication data. The layer dropping scheme we propose compares the representative values of each hidden layer with a threshold value. Furthermore, to guarantee the training accuracy, we store the gradients that are not transmitted to the parameter server in the worker’s local cache. When the value of gradients stored in the worker’s local cache is greater than the threshold, the gradients stored in the worker’s local cache are transmitted to the parameter server. The second is an efficient threshold selection method. Our threshold selection method computes the threshold by replacing the gradients with the L1 norm of each hidden layer. Our data optimization scheme reduces the communication time by about 81% and the total training time by about 70% in a 56 Gbit network environment

Comparison of international guidelines of dermal absorption tests used in pesticides exposure assessment for operators

The number of farmers who have suffered from non-fatal acute pesticide poisoning has been reported to vary from 5.7% to 86.7% in South Korea since 1975. Absorption through the skin is the main route of exposure to pesticides for farmers who operate with them. Several in vitro tests using the skins of humans or animal and in vivo tests using laboratory animals are introduced for the assessment of human dermal absorption level of pesticides. The objective of this study is to evaluate and compare international guidelines and strategies of dermal absorption assessments and to propose unique approaches for applications into pesticide registration process in our situation. Until present in our situation, pesticide exposure level to operator is determined just using default value of 10 as for skin absorption ratio because of data shortage. Dermal absorption tests are requested to get exposure level of pesticides and to ultimately know the safety of pesticides for operators through the comparison with the value of AOEL. When the exposure level is higher than AOEL, the pesticide cannot be approved. We reviewed the skin absorption test guidelines recommended by OECD, EFSA and EPA. The EPA recommends assessment of skin absorption of pesticides for humans through the TPA which includes all the results of in vitro human and animal and animal in vivo skin absorption studies. OECD and EFSA, employ a tiered approach, which the requirement of further study depends on the results of the former stage study. OECD guidelines accept the analysis of pesticide level absorbed through skin without radioisotope when the recovery using the non-labeled method is within 80~120%. Various factors are reviewed in this study, including the origin of skin (gender, animal species and sites of skin), thickness, temperature and, etc., which can influence the integrity of results

Profiling of testis-specific long noncoding RNAs in mice

Abstract Background Spermatogenesis, which is the complex and highly regulated process of producing haploid spermatozoa, involves testis-specific transcripts. Recent studies have discovered that long noncoding RNAs (lncRNAs) are novel regulatory molecules that play important roles in various biological processes. However, there has been no report on the comprehensive identification of testis-specific lncRNAs in mice. Results We performed microarray analysis of transcripts from mouse brain, heart, kidney, liver and testis. We found that testis harbored the highest proportion of tissue-specific lncRNAs (11%; 1607 of 14,256). Testis also harbored the largest number of tissue-specific mRNAs among the examined tissues, but the proportion was lower than that of lncRNAs (7%; 1090 of 16,587). We categorized the testis-specific lncRNAs and found that a large portion corresponded to long intergenic ncRNAs (lincRNAs). Genomic analysis identified 250 protein-coding genes located near (≤ 10 kb) 194 of the loci encoding testis-specific lincRNAs. Gene ontology (GO) analysis showed that these protein-coding genes were enriched for transcriptional regulation-related terms. Analysis of male germ cell-related cell lines (F9, GC-1 and GC-2) revealed that some of the testis-specific lncRNAs were expressed in each of these cell lines. Finally, we arbitrarily selected 26 testis-specific lncRNAs and performed in vitro expression analysis. Our results revealed that all of them were expressed exclusively in the testis, and 23 of the 26 showed germ cell-specific expression. Conclusion This study provides a catalog of testis-specific lncRNAs and a basis for future investigation of the lncRNAs involved in spermatogenesis and testicular functions