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The epidemiological evidence demonstrating the effect of disinfection by-products (DBPs) from drinking water on colon and rectal cancers is well documented. However, no systematic assessment has been conducted to assess the potential effect measure modification (EMM) in the relationship between DBPs and cancer. The objective of this paper is to conduct a systematic literature review to determine the extent to which EMM has been assessed in the relationship between DBPs in drinking water in past epidemiological studies. Selected articles (n = 19) were reviewed, and effect estimates and covariates that could have been used in an EMM assessment were gathered. Approximately half of the studies assess EMM (n = 10), but the majority of studies only estimate it relative to sex subgroups (n = 6 for bladder cancer and n = 2 both for rectal and colon cancers). Although EMM is rarely assessed, several variables that could have a potential modification effect are routinely collected in these studies, such as socioeconomic status or age. The role of environmental exposures through drinking water can play an important role and contribute to cancer disparities. We encourage a systematic use of subgroup analysis to understand which populations or territories are more vulnerable to the health impacts of DBPs.

Cryptosporidium and Giardia are protozoan parasites responsible for gastrointestinal illnesses in humans and in animal species. The main way these parasites are transmitted is by ingestion of their (oo)cysts in drinking water. Monitoring (oo)cysts in water sources is beneficial to evaluate the quality of raw water supplying treatment plants. Currently, the only standardized protocol to enumerate these parasites from water samples is United States Environmental Protection Agency (USEPA) Method 1623.1. With this method, we monitored three major water sources in Quebec over a year to assess temporal and geographical variations of these parasite (oo)cysts. These three water sources have independent watersheds despite being in the same region. We found a general pattern for Giardia, with high concentrations of cysts during cold and transition periods, and significantly lower concentrations during the warm period. Cryptosporidium's concentration was more variable throughout the year. Statistical correlations (Pearson's correlation coefficients) were established between the concentration of each parasite and various environmental parameters. The three study sites each showed unique factors correlating with the presence of both protozoa, supporting the idea that each water source must be seen as a unique entity with its own particular characteristics and therefore, must be monitored independently. Although some environmental parameters could be interesting proxies to the parasitic load, no parameter was strongly correlated throughout the whole sampling year and none of the parameters could be used as a single proxy for all three studies sources. [ABSTRACT FROM AUTHOR]

The Deep Underground Neutrino Experiment (DUNE) will be a world-class neutrino observatory and nucleon decay detector aiming to address some of the most fundamental questions in particle physics. With a modular liquid argon time-projection chamber (LArTPC) of 40 kt fiducial mass, the DUNE far detector will be able to reconstruct neutrino interactions with an unprecedented resolution. With no triggering and no zero suppression or compression, the total raw data volume would be of order 145 EB/year. Consequently, fast and affordable reconstruction methods are needed. Several state-of-theart methods are focused on machine learning (ML) approaches to identify the signal within the raw data or to classify the neutrino interaction during the reconstruction. One of the main advantages of using those techniques is that they will reduce the computational cost and time compared to classical strategies. Our plan aims to go a bit further and test the implementation of those techniques on an accelerator board. In this work, we present the accelerator board used, a commercial off-the-shelf (COTS) hardware for fast deep learning (DL) inference based on an FPGA, and the experimental results obtained outperforming more traditional processing units. The FPGA-based approach is planned to be eventually used for online reconstruction.

This paper presents the Detector Control System (DCS) that has been designed and implemented for the NP04 experiment at CERN. NP04, also known as protoDUNE Single Phase (SP), aims at validating the engineering processes and detector performance of a large Liquid Argon (LAr) Time Projection Chamber (TPC) in view of the DUNE experiment. Built at the CERN Neutrino Platform (NP) facilities, it started to operate in September 2018 after two years of construction and commissioning, using a tertiary beam of the CERN SPS accelerator. After an overall description of the distributed control architecture that has been chosen for the control of this experiment, focus will be put on describing the software system design, based on the CERN control frameworks UNICOS and JCOP (built on top of WINCC OA). The knowledge acquired during the operation of the DCS is discussed and future work are presented.