Context: Polymeric nanoparticles (NPs) have been used frequently as drug delivery vehicles. Surface modification of polymeric NPs with specific ligands defines a new biological identity, which assists in targeting of the nanocarriers to specific cancers cells. Objective: The aim of this study is to develop a kind of modified vector which could target the cancer cells through receptor-mediated pathways to increase the uptake of doxorubicin (DOX). Methods: Folate (FA)-conjugated PEG-PE (FA-PEG-PE) ligands were used to modify the polymeric NPs. The modification rate was optimized and the physical-chemical characteristics, in vitro release, and cytotoxicity of the vehicle were evaluated. The in vivo therapeutic effect of the vectors was evaluated in human nasopharyngeal carcinoma KB cells baring mice by giving each mouse 100 ml of 10 mg/kg different solutions. Results: FA-PEG-PE-modified NPs/DOX (FA-NPs/DOX) have a particle size of 229 nm, and 86% of drug loading quantity. FA-NPs/DOX displayed remarkably higher cytotoxicity (812 mm 3 tumor volume after 13 d of injection) than non-modified NPs/DOX (1290 mm 3 ) and free DOX solution (1832 mm 3 ) in vivo. Conclusion: The results demonstrate that the modified drug delivery system (DDS) could function comprehensively to improve the efficacy of cancer therapy. Consequently, the system was shown to be a promising carrier for delivery of DOX, leading to the efficiency of antitumor therapy.

As the primary approach to deriving decision-support insights, automated recurring routine analytic jobs account for a major part of cluster resource usages in modern enterprise data warehouses. These recurring routine jobs usually have stringent schedule and deadline determined by external business logic, and thus cause dreadful resource skew and severe resource over-provision in the cluster. In this paper, we present Grosbeak, a novel data warehouse that supports resource-aware incremental computing to process recurring routine jobs, smooths the resource skew, and optimizes the resource usage. Unlike batch processing in traditional data warehouses, Grosbeak leverages the fact that data is continuously ingested. It breaks an analysis job into small batches that incrementally process the progressively available data, and schedules these small-batch jobs intelligently when the cluster has free resources. In this demonstration, we showcase Grosbeak using real-world analysis pipelines. Users can interact with the data warehouse by registering recurring queries and observing the incremental scheduling behavior and smoothed resource usage pattern.

We consider accelerating machine learning (ML) inference queries on unstructured datasets. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable with classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) assume independence among the query predicates, build a proxy model for each predicate offline, and rewrite a new query by injecting these cheap proxy models in the front of the expensive ML UDFs. In such a manner, unlikely inputs that do not satisfy query predicates are filtered early to bypass the ML UDFs. We show that enforcing the independence assumption in this context may result in sub-optimal plans. In this paper, we propose CORE, a query optimizer that better exploits the predicate correlations and accelerates ML inference queries. Our solution builds the proxy models online for a new query and leverages a branch-and-bound search process to reduce the building costs. Results on three real-world text, image and video datasets show that CORE improves the query throughput by up to 63% compared to PP and up to 80% compared to running the queries as it is.

A long-running analytic task on big data often leaves a developer in the dark without providing valuable feedback about the status of the execution. In addition, a failed job that needs to restart from scratch can waste earlier computing resources. An effective method to address these issues is to allow the developer to debug the task during its execution, which is unfortunately not supported by existing big data solutions. In this paper we develop a system called Amber that supports responsive debugging during the execution of a workflow task. After starting the execution, the developer can pause the job at will, investigate the states of the cluster, modify the job, and resume the computation. She can also set conditional breakpoints to pause the execution when certain conditions are satisfied. In this way, the developer can gain a much better understanding of the run-time behavior of the execution and more easily identify issues in the job or data. Amber is based on the actor model, a distributed computing paradigm that provides concurrent units of computation using actors. We give a full specification of Amber, and implement it on top of the Orleans system. Our experiments show its high performance and usability of debugging on computing clusters.

We are developing Texera, an open source system that allows users to perform data analysis on a computing cluster using a GUI-based workflow. A unique functionality of the system is its support for interactive and responsive debugging on dataflows during their execution, while still being scalable and fault tolerant. In particular, users can pause/resume a workflow, investigate the state of operators, change the behavior of an operator, and set conditional breakpoints. In this way, a user will not feel "in the dark" during the long-running execution of an analytics task, a problem faced by other big data processing frameworks. In this demonstration we show this powerful functionality in Texera.