Transferrin-Based Therapeutics and Analytical Methods to Characterize Them

Modern development of medicine requires detailed characterization by state-of-the art analytical techniques that can be used to analyze covalent structure, conformations and protein-receptor interaction to quantitatively measure biodistribution of protein therapeutics. Mass spectrometry has already become an indispensable tool facilitating all stages of protein drug development. Particularly, this work has demonstrated the tremendous potential of electrospray ionization (ESI) mass spectrometry (MS) in this arena by providing invaluable information beyond mass measurement that can be used to optimize protein drug conjugate structures during early stages of development, and to further catalyze drug design efforts. Additionally, a new sensitive and selective method that uses metal tracers and inductively coupled plasma (ICP) MS developed in our lab has been successfully applied for quantitating exogenous transferrin (Tf) and Tf-based drugs in biological tissues and fluids. Furthermore, ICP-MS based method using metal tracer in combination with size exclusion chromatography (SEC) method proved to be able to probe into protein stability post-injection and to yield useful data not accessible by other methods. For the first time a small soluble protein aggregation of injected protein drug was studied in live animals. Finally, a simple and cost-effective 18O labeling-based method has been developed for quantitating lysine modification sites of protein drug conjugates and has been successfully applied for N-succinimidyl-S-acetylthioacetate (SATA)-Lysozyme (Lz) conjugate

Using Extensive Reading to Improve First Year Students Learner Autonomy

Extensive Reading (ER) has been a hot topic among the scholars all over the world due to the benefits it brings to studentsrsquo study. It is often argued to improve learner autonomy, vocabulary learning, writing, attitude towards reading and so forth. This paper reviews the literature relevant to the above issues and indicates the challenges of implementing Extensive Reading into the language classrooms

Does BLEU Score Work for Code Migration?

Statistical machine translation (SMT) is a fast-growing sub-field of computational linguistics. Until now, the most popular automatic metric to measure the quality of SMT is BiLingual Evaluation Understudy (BLEU) score. Lately, SMT along with the BLEU metric has been applied to a Software Engineering task named code migration. (In)Validating the use of BLEU score could advance the research and development of SMT-based code migration tools. Unfortunately, there is no study to approve or disapprove the use of BLEU score for source code. In this paper, we conducted an empirical study on BLEU score to (in)validate its suitability for the code migration task due to its inability to reflect the semantics of source code. In our work, we use human judgment as the ground truth to measure the semantic correctness of the migrated code. Our empirical study demonstrates that BLEU does not reflect translation quality due to its weak correlation with the semantic correctness of translated code. We provided counter-examples to show that BLEU is ineffective in comparing the translation quality between SMT-based models. Due to BLEU's ineffectiveness for code migration task, we propose an alternative metric RUBY, which considers lexical, syntactical, and semantic representations of source code. We verified that RUBY achieves a higher correlation coefficient with the semantic correctness of migrated code, 0.775 in comparison with 0.583 of BLEU score. We also confirmed the effectiveness of RUBY in reflecting the changes in translation quality of SMT-based translation models. With its advantages, RUBY can be used to evaluate SMT-based code migration models.Comment: 12 pages, 5 figures, ICPC '19 Proceedings of the 27th International Conference on Program Comprehensio

Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods

Required flows for aquatic ecosystems in Ma River, Vietnam

Ecological flow requirements for the Ma River in dry season were assessed in three reaches of Ma – Buoi, Ma – Len and Ma – Chu. 5 indictor fish species was chosen based on biodiversity survey and roles of those species in aquatic ecosystem as well as local communities. Biological and hydrological data (dry season of 2016- 2017) and 35 year recorded hydrological data were collected and analyzed as input data for a physical habitat model River HYdraulic and HABitat SImulation Model – RHYHABSIM. Model results shown that the optimal flows of the reaches were very much higher compare with the minimum annual low flow - MALF. In this study, MALF7day were applied to calculate the recommended minimum flows of the three reaches. The recommended required minimum flows for Ma – Buoi, Ma – Len and Ma – Chu reaches were 51 m3/s, 49 m3/s and 61 m3/s, respectively. It must be stressed that this study only assessed whether or not there is enough habitat available for the river to sustain a healthy ecosystem