Jianlin Jiang

To assess the source and public health significance of Cryptosporidium oocyst contamination in storm runoff, a PCR-restriction fragment length polymorphism technique based on the small-subunit rRNA gene was used in the analysis of 94 storm water samples collected from the Malcolm Brook and N5 stream basins in New York over a 3-year period. The distribution of Cryptosporidium in this study was compared with the data obtained from 27 storm water samples from the Ashokan Brook in a previous study. These three watersheds represented different levels of human activity. Among the total of 121 samples analyzed from the three watersheds, 107 were PCR positive, 101 of which (94.4%) were linked to animal sources. In addition, C. hominis (W14) was detected in six samples collected from the Malcolm Brook over a 2-week period. Altogether, 22 Cryptosporidium species or genotypes were found in storm water samples from these three watersheds, only 11 of which could be attributed to known species/groups of animals. Several Cryptosporidium spp. were commonly found in these three watersheds, including the W1 genotype from an unknown animal source, the W4 genotype from deer, and the W7 genotype from muskrats. Some genotypes were found only in a particular watershed. Aliquots of 113 samples were also analyzed by the Environmental Protection Agency (EPA) Method 1623; 63 samples (55.7%) were positive for Cryptosporidium by microscopy, and 39 (78%) of the 50 microscopy-negative samples were positive by PCR. Results of this study demonstrate that molecular techniques can complement traditional detection methods by providing information on the source of contamination and the human-infective potential of Cryptosporidium oocysts found in water.

Extraction of high-quality DNA is a key step in PCR detection of Cryptosporidium and other pathogens in environmental samples. Currently, Cryptosporidium oocysts in water samples have to be purified from water concentrates before DNA is extracted. This study compared the effectiveness of six DNA extraction methods (DNA extraction with the QIAamp DNA minikit after oocyst purification with immunomagnetic separation and direct DNA extraction methods using the FastDNA SPIN kit for soil, QIAamp DNA stool minikit, UltraClean soil kit, or QIAamp DNA minikit and the traditional phenol-chloroform technique) for the detection of Cryptosporidium with oocyst-seeded samples, DNA-spiked samples, and field water samples. The study also evaluated the effects of different PCR facilitators (nonacetylated bovine serum albumin, the T4 gene 32 protein, and polyvinylpyrrolidone) and treatments (the use of GeneReleaser or ultrafiltration) for the relief from or removal of inhibitors of PCR amplification. The results of seeding and spiking studies showed that PCR inhibitors were presented in all DNA solutions extracted by the six methods. However, the effect of PCR inhibitors could be relieved significantly by the addition of 400 ng of bovine serum albumin/mul or 25 ng of T4 gene 32 protein/mul to the PCR mixture. With the inclusion of bovine serum albumin in the PCR mixture, DNA extracted with the FastDNA SPIN kit for soil without oocyst isolation resulted in PCR performance similar to that produced by the QIAamp DNA minikit after oocysts were purified by immunomagnetic separation.

Building Information Modeling (BIM) is envisioned as an indispensable opportunity in the architecture, engineering, and construction (AEC) industries as a revolutionary technology and process. Smart construction relies on BIM for manipulating information flow, data flow, and management flow. Currently, BIM model has been explored mainly for information construction and utilization, but rare works pay efforts to information security, e.g., critical model audit and sensitive model exposure. Moreover, few BIM systems are proposed to chase after upcoming computing paradigms, such as mobile cloud computing, big data, blockchain, and Internet of Things. In this paper, we make the first attempt to propose a novel BIM system model called bcBIM to tackle information security in mobile cloud architectures. More specifically, bcBIM is proposed to facilitate BIM data audit for historical modifications by blockchain in mobile cloud with big data sharing. The proposed bcBIM model can guide the architecture design for further BIM information management system, especially for integrating BIM cloud as a service for further big data sharing. We propose a method of BIM data organization based on blockchains and discuss it based on private and public blockchain. It guarantees to trace, authenticate, and prevent tampering with BIM historical data. At the same time, it can generate a unified format to support future open sharing, data audit, and data provenance.

Six Cryptosporidium spp. were found in 50 of 179 Milwaukee wastewater samples collected weekly over a year. Of the eight subtypes of Cryptosporidium hominis and Cryptosporidium parvum present, allele Ib was found in 14 of 16 samples, and its sequence was identical to that of the subtype in human samples from the 1993 Milwaukee outbreak of cryptosporidiosis.

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model updates to a central server for aggregation. Since the server has no visibility into the process of generating the updates, collaborative learning is vulnerable to poisoning attacks where a malicious client can generate a poisoned update to introduce backdoor functionality to the joint model. The existing solutions for detecting poisoned updates, however, fail to defend against the recently proposed attacks, especially in the non-IID (independent and identically distributed) setting. In this article, we present a novel defense scheme to detect anomalous updates in both IID and non-IID settings. Our key idea is to realize client-side cross-validation, where each update is evaluated over other clients' local data. The server will adjust the weights of the updates based on the evaluation results when performing aggregation. To adapt to the unbalanced distribution of data in the non-IID setting, a dynamic client allocation mechanism is designed to assign detection tasks to the most suitable clients. During the detection process, we also protect the client-level privacy to prevent malicious clients from knowing the participations of other clients, by integrating differential privacy with our design without degrading the detection performance. Our experimental evaluations on three real-world datasets show that our scheme is significantly robust to two representative poisoning attacks.